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Applied Domains

Bijux scientific repositories treat data preparation, evidence selection, and interpretation as first-class product work. Analysis begins only after source identity, inclusion decisions, normalization, and provenance are made visible.

Scientific Evidence Chain

flowchart LR
    source["Source records and literature"] --> curate["Curate and qualify"]
    curate --> normalize["Normalize and reconcile identity"]
    normalize --> model["Model domain relationships"]
    model --> analyze["Analyze and compare"]
    analyze --> interpret["Interpret with limitations"]
    interpret --> publish["Publish data, maps, or evidence books"]
    publish --> reproduce["Reconstruct from declared inputs"]

Every transition can change the conclusion. Curation is therefore not a preliminary clerical activity; it is part of the evidence model.

Domain Systems

Bijux Canon — governed knowledge. Heterogeneous sources move through deterministic ingest, structured indexing, retrieval, reasoning, and controlled runtime acceptance. Public surfaces include indexed knowledge, query behavior, reasoning contracts, and compatibility boundaries.

Bijux Proteomics — protein evidence and discovery. Database preparation, entity reconciliation, evidence lineage, validation, and analysis contracts support packages, knowledge assets, and laboratory-facing workflows.

Bijux Pollenomics — pollen evidence in place and time. Source curation, taxonomic and spatial reconciliation, archaeology/eDNA/aDNA context, and report preparation support curated databases, maps, atlases, and evidence-backed interpretation.

Bijux Phylogenetics — comparative evidence across lineages. Sequence and trait curation, phylogenetic comparative models, and alternative explanations support comparative analyses and evidence books.

Curation As Evidence

A curated dataset expresses scientific judgment through inclusion, exclusion, normalization, and reconciliation.

Curation decision Why it can change the result
source selection coverage and publication bias enter before analysis begins
entity identity synonyms, accessions, taxa, sites, or assemblies can otherwise fragment one entity or merge distinct ones
unit and schema normalization incomparable values can appear compatible when transformation rules are hidden
missingness treatment absence, unknown, below-detection, and not-applicable carry different meanings
conflict resolution choosing one source over another changes the authoritative record
exclusion criteria filtering can change the population to which a conclusion applies
version pinning upstream drift can make an older result impossible to reconstruct

The curation record should preserve these decisions alongside the resulting database rather than presenting the database as raw fact.

Make The Measurement Model Visible

Curated entities do not become observations by themselves. A defensible analysis records how the biological target was sampled, how the assay produced measurements, and which transformations separated those measurements from the reported estimate.

flowchart LR
    target["Target population"] --> sample["Sample and study design"]
    sample --> assay["Assay and instrument"]
    assay --> observed["Observed values and missingness"]
    observed --> qc["Quality control and error model"]
    qc --> estimate["Estimate with uncertainty"]
    estimate --> claim["Bounded scientific claim"]
Design concern Evidence needed before interpretation
sampling target population, recruitment or collection rule, exclusions, and coverage gaps
biological and technical replicates replicate identity, dependence structure, and aggregation rule
controls positive, negative, blank, reference, and failure-control behavior where applicable
batch and instrument run order, platform identity, calibration, drift, and correction policy
missing observations reason classes, censoring or detection assumptions, and retained denominator
calibration and uncertainty reference material, fitted range, residual behavior, precision, and limit of use

A schema-valid table can still encode a confounded design. Reconstructable bytes establish custody; they do not establish that treatment, batch, instrument, site, or time can be distinguished by the model.

Name The Estimand Before The Estimate

An estimate is interpretable only relative to the quantity it is intended to represent. The evidence record should define the unit, population, contrast, time horizon, and handling of events that prevent or alter observation before presenting a numerical result.

Estimand component Question that must be answerable
unit is inference about a measurement, sample, subject, site, taxon, lineage, or study?
population which members and conditions are eligible for the intended quantity?
treatment or exposure what intervention, state, or comparison is defined, and when?
outcome what is measured, at which time, in which units, and under which detection process?
intercurrent event how are dropout, assay failure, death, migration, contamination, or protocol change handled?
aggregation which dependence, weighting, repeated measures, and uncertainty produce the reported scale?

Changing any component can change the scientific question even when the same rows and software are used. A precise confidence interval around an unnamed or shifted estimand is not stronger evidence; it is a precise answer to an ambiguous question.

Protect Evaluation From Leakage And Multiplicity

Discovery, model selection, calibration, threshold selection, and final evaluation have different authorities. Reusing the same evidence across those roles can produce an optimistic result even when every computation is deterministic.

  • identify the population used to develop the method separately from the population used to estimate performance;
  • keep subjects, sites, batches, instruments, or lineages together when their dependence would leak across a split;
  • define the family of tested hypotheses, contrasts, or candidate models so an isolated favorable result is not presented without its search burden;
  • label exploratory findings as exploratory until a declared confirmation route has been evaluated;
  • treat performance in a new population as a separate transfer claim, not as a property inherited from internal validation.

An external benchmark is useful only when its truth source, overlap checks, observation unit, exclusions, and uncertainty are visible. The benchmark name alone does not establish independence.

Keep Source, Evidence, Analysis, And Claim Populations Distinct

Scientific systems repeatedly narrow and transform a population. Each transition needs its own denominator and refusal record.

flowchart LR
    discover["Discovered source population"] --> captured["Captured source members"]
    captured --> governed["Governed evidence objects"]
    governed --> eligible["Claim-eligible population"]
    eligible --> analyzed["Analyzed observations"]
    analyzed --> interpreted["Claim observations"]
    interpreted --> published["Published members or statements"]
    captured -. "unavailable or rejected" .-> negative["Negative evidence ledger"]
    governed -. "conflict or unresolved identity" .-> negative
    eligible -. "excluded by rule" .-> negative
    analyzed -. "failed or missing output" .-> negative
Population Identity that must survive Common overclaim
discovered sources query, registry, bibliography, release, and discovery time treating search results as captured evidence
captured members source-native key, payload or locator, version, and access context treating collection as scientific acceptance
governed objects normalized identity, fact ownership, lineage, conflicts, and curation decision treating normalization as truth resolution
eligible objects named claim, inclusion and exclusion rules, missingness and precision hiding selection behind the analyzed count
analyzed observations method, configuration, failures, diagnostics, and complete result inventory reporting only successful rows
published members manifestation rule, caveat, revision, and stable public identity treating visibility as universal support

Counts are comparable only when their observation unit, identity namespace, scope, eligibility, missingness, and revision agree. A larger captured database can legitimately produce a smaller public product when the publication burden is stricter.

Propagate Evidence Corrections Without Rewriting History

A source retraction, taxonomy correction, changed protein accession, revised coordinate, or invalidated analytical output can affect several descendants. The correction should follow explicit dependency edges.

flowchart LR
    finding["Corrected or withdrawn evidence"] --> objects["Affected governed objects"]
    objects --> analyses["Dependent analyses"]
    analyses --> claims["Claim verdicts and limitations"]
    claims --> products["Reports, maps, databases, recommendations"]
    products --> consumers["Consumer notice or migration"]

Preserve the previous record, the correction reason, old and new identities, affected descendants, and the decision that supersedes or withdraws each claim. Regenerating a polished output without a correction relation destroys the evidence needed to understand what changed.

Interpretation Boundary

flowchart TD
    observation["Observed or curated evidence"] --> method["Declared method"]
    method --> signal["Estimated signal"]
    signal --> alternatives["Alternative explanations"]
    alternatives --> conclusion["Qualified conclusion"]
    uncertainty["Coverage, bias, and uncertainty"] --> conclusion

A result becomes trustworthy through qualification, not certainty language. Scientific pages should distinguish:

  • observation from inference;
  • correlation from mechanism;
  • model support from proof;
  • signal absence from absence of evidence;
  • regional or taxonomic scope from universal claims;
  • exploratory findings from evidence ready for downstream use.

Evidence And Action Remain Separate

Proteomics makes a general scientific principle visible: computation, execution, grounding, recommendation, and observed consequence are different records with different authorities.

flowchart LR
    calculate["Scientific calculation"] --> execute["Execution record"]
    execute --> ground["Support and contradiction"]
    ground --> decide["Policy-bound recommendation"]
    decide --> observe["Laboratory consequence"]
    observe --> revise["Revised evidence state"]
    calculate -. failed burden .-> narrow["Narrow or refuse"]
    ground -. contradiction .-> narrow
    decide -. instability .-> narrow
    observe -. infeasible .-> narrow

The chain is asymmetric. Any missing or failed burden can narrow the public claim; success later in the chain cannot upgrade weak evidence earlier in the chain. An observed outcome informs the next decision without rewriting the record that led to the original action.

Shared Capabilities, Local Meaning

The scientific repositories can consume common execution, knowledge, and documentation capabilities. They retain authority over their domain semantics.

  • bijux-core can provide deterministic execution and evidence mechanics;
  • bijux-canon can provide knowledge ingest and retrieval contracts;
  • bijux-atlas can provide dataset and service-delivery patterns;
  • bijux-std can provide shared repository and documentation contracts.

None of those layers decides whether a protein relationship, pollen signal, or phylogenetic conclusion is scientifically supported. That responsibility stays with the domain repository and its evidence.

Reproducible Publication

A scientific publication route is complete when a reader can connect:

  1. the claim to the analysis;
  2. the analysis to parameters, models, and software identity;
  3. the analysis input to a curated dataset version;
  4. the curated dataset to source records and curation decisions;
  5. the output to stated uncertainty and limitations.

Maps, dashboards, and polished reports are delivery surfaces, not substitutes for this chain.

Reproducibility also has a ceiling. Reconstructing identical bytes shows that the declared computation is repeatable; it does not establish that the source population was unbiased, the method was valid for a new context, or the interpretation remains current after upstream evidence changes.

Explore The Domains