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Low genotyping error rates in wild ungulate faeces sampled in winter

2004· article· en· W2004213116 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueMolecular Ecology Notes · 2004
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGenetic and phenotypic traits in livestock
Canadian institutionsUniversité de Sherbrooke
FundersOffice National de la Chasse et de la Faune SauvageCentre National de la Recherche Scientifique
KeywordsGenotypingMicrosatelliteBiologyFecesUngulateGenotypePopulationVeterinary medicineZoologyEcologyGeneticsDemographyAllele

Abstract

fetched live from OpenAlex

Abstract We show that Alpine ibex ( Capra ibex ) and Corsican mouflon ( Ovis musimon ) faeces yield useful DNA for microsatellite analysis, however, we detected higher genotyping error rates for spring faeces than for winter faeces. We quantified the genotyping error rate by repeatedly genotyping four microsatellites. Respectively, 99 and 95% of mouflon and ibex genotyping repetitions provided a correct genotype using winter samples, whereas spring samples provided only 52 and 59% correct genotypes. Thus, before starting a noninvasive study, we recommend that researchers conduct a pilot study to quantify genotyping error rates for each season, population and species to be studied.

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.483
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.010
GPT teacher head0.255
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