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Record W4409760500 · doi:10.5038/2074-1235.45.2.1230

Geographical Variation in Incubation Shift Length of Ancient Murrelets Synthliborapmphus Antiquus Determined from Geolocator Devices

2017· article· en· W4409760500 on OpenAlexfundno aff
Anthony J. Gaston, Yuriko Hashimoto, Laurie Wilson

Bibliographic record

VenueMarine ornithology · 2017
Typearticle
Languageen
FieldComputer Science
TopicHand Gesture Recognition Systems
Canadian institutionsnot available
FundersEnvironment and Climate Change Canada
KeywordsSeabirdOrnithologyIncubationVariation (astronomy)ZoologyGeographyBiologyEcologySouthern HemispherePhysicsAstronomy

Abstract

fetched live from OpenAlex

Incubation shift lengths were estimated from geolocator devices attached to 48 Ancient Murrelets Synthliboramphus antiquus at four colonies in Haida Gwaii, British Columbia, during 2014 and retrieved during 2015.Light-level data were used to determine the timing of colony departures in 2014 and the start of incubation in 2015, and to measure the length of incubation shifts.Incubation started and ended 12 d later at colonies on the west coast of Haida Gwaii than at those on the east coast.First at-sea shifts after devices were attached (mean 3.6 d) were longer than later shifts (2.8 d), and longer than corresponding mate shifts (2.9 d), suggesting that attachment had some effect on behaviour.However, by the time of colony departure, shift lengths were unaffected by the devices.During 2014, excluding the last shift before departure, most shifts at colonies on the east coast were 1-3 d, whereas those at west coast colonies were 3-5 d.Shifts during 2015 were also longer at west coast than at east coast colonies, although sample sizes were smaller because time between start of incubation and recapture was mostly brief.This is the first demonstration of regional variation in incubation shifts among Ancient Murrelet populations.Although geolocators are generally used to study long-distance movements in seabirds, our results support to the idea that they can provide substantial additional information on the breeding biology of birds.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.048
Threshold uncertainty score0.634

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 science0.0010.000
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 head0.014
GPT teacher head0.256
Teacher spread0.242 · 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.

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

Citations0
Published2017
Admission routes1
Has abstractyes

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