MétaCan
Menu
Back to cohort
Record W2124512621 · doi:10.1648/0273-8570-76.4.357

Breeding chronology of Marbled Murrelets varies between coastal and inshore sites in southern British Columbia

2005· article· en· W2124512621 on OpenAlexaffabout
Laura McFarlane Tranquilla, Nadine Parker, Russell W. Bradley, David B. Lank, Elizabeth Krebs, Lynn W. Lougheed, Cecilia Lougheed

Bibliographic record

VenueJournal of Field Ornithology · 2005
Typearticle
Languageen
FieldEnvironmental Science
TopicAvian ecology and behavior
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsSound (geography)Nest (protein structural motif)GeographyFisheryChronologyFish <Actinopterygii>OceanographyArchaeologyBiologyGeology

Abstract

fetched live from OpenAlex

We used four methods to compare the breeding chronologies of Marbled Murrelet at two sites at similar latitudes in British Columbia: Desolation Sound on the mainland, inshore of the Strait of Georgia, and Clayoquot Sound on the west coast of Vancouver Island. At both sites, we estimated breeding chronologies from the timing of (1) nest initiation dates determined by radio-telemetry, (2) the chick feeding period determined from observations of fish-holding adults, (3) hatch dates determined from observations of juveniles on the water, and (4) brood patch scores determined from captured birds. At Desolation Sound, these methods each produced a similar distribution of nesting dates, but at Clayoquot Sound, the distribution of nesting dates of radio-tracked birds were substantially biased towards later nests. Despite these methodological difficulties, we found that Marbled Murrelets at Desolation Sound bred ca. 30 d later than at Clayoquot Sound. Regional differences in breeding chronology of this magnitude, if not properly calibrated, would bias estimates of peak inland activity, and should be considered in forestry operations, the interpretation of census data, and the design of monitoring programs.

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 score0.077
Threshold uncertainty score0.998

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)0.0030.000

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 head0.011
GPT teacher head0.228
Teacher spread0.217 · 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

Citations19
Published2005
Admission routes2
Has abstractyes

Explore more

Same venueJournal of Field OrnithologySame topicAvian ecology and behaviorFrench-language works237,207