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Record W2904149480 · doi:10.1139/cjz-2018-0172

Interannual repeatability of eggshell phenotype in individual female Common Murres (<i>Uria</i><i> aalge</i>)

2018· article· en· W2904149480 on OpenAlexvenueno aff
Márk E. Hauber, Alec B. Luro, C.J. McCarty, Ketti Barateli, Phillip Cassey, Erpur Snær Hansen, James Dale

Bibliographic record

VenueCanadian Journal of Zoology · 2018
Typearticle
Languageen
FieldEnvironmental Science
TopicAvian ecology and behavior
Canadian institutionsnot available
FundersU.S. Department of Agriculture
KeywordsUria aalgeBiologySeabirdEggshellZoologyNest (protein structural motif)EcologyCharadriiformesPaternal carePredationGenetics

Abstract

fetched live from OpenAlex

The recognition of own progeny is critical in group-living organisms that provide parental care for their young. The colonial seabird Common Murre (Uria aalge (Pontoppidan, 1763); also known as the Common Guillemot) does not build a nest, so direct cues must be available for the parents to recognize their own egg. However, only anecdotal evidence exists that, as seen in other avian lineages where examined, eggshells of Common Murres are also consistent in most aspects of their appearance between different breeding attempts by each female. Using digital photography, we quantified several eggshell traits of a handful of captive Common Murres across multiple years. Individual female Common Murre eggs were significantly repeatable in background colour, maculation coverage, spot shape, and shell size. Laying individually consistent and recognizable eggshells across breeding attempts may benefit Common Murres by reducing both the cost of relearning and the risk of misidentifying their own eggs. More generally, these data also add to the growing knowledge of individually consistent eggshell genesis by the avian reproductive system.

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)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.017
GPT teacher head0.253
Teacher spread0.235 · 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 designObservational
Domainnot available
GenreEmpirical

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

Citations15
Published2018
Admission routes1
Has abstractyes

Explore more

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