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<scp>papa</scp> (package for the analysis of parental allocation): a computer program for simulated and real parental allocation

2002· article· en· W2011796376 on OpenAlexafffund
Pierre Duchesne, Marie‐Hélène Godbout, Louis Bernatchez

Bibliographic record

VenueMolecular Ecology Notes · 2002
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGenetic and phenotypic traits in livestock
Canadian institutionsUniversité Laval
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsOffspringComputer scienceResource allocationSet (abstract data type)StatisticsBiologyGeneticsMathematics

Abstract

fetched live from OpenAlex

Abstract papa is a parental pair allocation and simulator program. The allocation method is based on the likelihood of a parental pair producing the multilocus genotype found in the offspring being tested, which will be referred to as the breeding likelihood . Estimated level and structure of allele transmission errors in offspring are parameters fed into the allocation procedure. The embodied Monte‐Carlo simulator also allows modelling of many allocation conditions, including transmission error and the estimated proportion of missing parents. Simulations may be run prior to the collection of real parents in order to define the minimal set of loci that is necessary to reach a desired level of allocation success. Post‐collection simulations aim at statistically assessing the reliability of nonsimulated allocations. Simulations output values for several random variables.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Software · Consensus signal: none
Teacher disagreement score0.118
Threshold uncertainty score0.394

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.002
Open science0.0030.002
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.1180.031

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.013
GPT teacher head0.258
Teacher spread0.245 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreSoftware

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations182
Published2002
Admission routes2
Has abstractyes

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Same venueMolecular Ecology NotesSame topicGenetic and phenotypic traits in livestockFrench-language works237,207