MétaCan
Menu
Back to cohort
Record W2037847172 · doi:10.1139/z06-155

Eggs of spruce grouse dry at a faster rate than those of ruffed grouse

2006· article· en· W2037847172 on OpenAlexafffundvenueabout
James F. Bendell, L. I. Bendell-Young

Bibliographic record

VenueCanadian Journal of Zoology · 2006
Typearticle
Languageen
FieldImmunology and Microbiology
TopicBird parasitology and diseases
Canadian institutionsSimon Fraser University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsGrouseNest (protein structural motif)BiologyHumidityIncubationAnimal scienceEcologyMoistureRelative humidityHabitatGeographyMeteorology

Abstract

fetched live from OpenAlex

We measured the rate of water loss and pore density of eggs of spruce grouse ( Canachites canadensis canace (L., 1766)) and ruffed grouse ( Bonasa umbellus togata (L., 1766)) from different parts of their range in Ontario. Eggs were dried in enclosed glass jars over Drierite ® and in paper trays in open air at room temperature and humidity. Eggs were weighed to the nearest 0.01 g every 2–4 days and the change in mass was measured as water loss. Pores of shells were counted (pores/cm 2 ) in the blunt, middle, and pointed sections of the egg. Eggs of spruce grouse lost water at a faster rate in Drierite ® and in open air and had a greater density of pores than eggs of ruffed grouse. Rates of water loss were constant and varied inversely with ambient humidity, with the difference between species greatest in open air. Eggs late in incubation of ruffed grouse dried at a faster rate than those early in incubation in Drierite ® . The adaptations of eggs of each grouse to the moisture of the nest may help explain their distribution, density, and habitat and nest-site selections, as well as behavioural aspects of the nesting hen. Both, especially the spruce grouse, may be good indictors of climate change.

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 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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.078
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
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 head0.010
GPT teacher head0.231
Teacher spread0.221 · 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 teacher head, not a consensus.

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

Citations5
Published2006
Admission routes4
Has abstractyes

Explore more

Same venueCanadian Journal of ZoologySame topicBird parasitology and diseasesFrench-language works237,207