MétaCan
Menu
Back to cohort
Record W4391696681 · doi:10.3354/esr01315

Long-term migratory alterations to whooping crane arrival and departure on the wintering and staging grounds

2024· article· en· W4391696681 on OpenAlexaffabout
MJ Butler, Mark T. Bidwell

Bibliographic record

VenueEndangered Species Research · 2024
Typearticle
Languageen
FieldEngineering
TopicTransportation Safety and Impact Analysis
Canadian institutionsEnvironment and Climate Change Canada
Fundersnot available
KeywordsTerm (time)GeographyFisheryBiologyPhysics

Abstract

fetched live from OpenAlex

Climate change can result in alterations to avian behavior, particularly in migratory species. We assessed long-term changes in the endangered whooping crane Grus americana migration phenology in response to temperature, precipitation, and other determinants of migratory behavior. We modeled timing of abundance peaks on the Texas wintering grounds as a function of date and year. During spring and fall migration in central Saskatchewan, we modeled timing of earliest and latest observations, and period of occurrence between them, as a function of year, weather, and wheat production. During winters 1950-2010, the peak abundance period (≥90% of population) shortened. In winter 1950-1951, the peak was 28 November-12 March, but by winter 2010-2011, it was 18 December-20 February. We predict it will shrink to 2 January-6 February by winter 2035-2036. During fall migration 1972-2021, the period cranes occurred in central Saskatchewan lengthened by 20.3 d. In 1971, cranes arrived by 16 September and departed by 17 October, but by 2021 they arrived 12 d earlier (4 September) and departed 17 d later (3 November). We predict a lengthened period of occurrence of 63.8 d by fall 2035 (arrival by 1 September, departure by 8 November). During spring migration 1979-2021, there were no trends in migration phenology (mean period of occurrence was 32 d). Alterations in migration phenology may require modified conservation approaches and consideration of new conservation opportunities. For example, these changes may reduce time cranes spend on the wintering grounds, requiring greater investment in stopover areas.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.835
Threshold uncertainty score0.457

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 head0.061
GPT teacher head0.332
Teacher spread0.271 · 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

Citations1
Published2024
Admission routes2
Has abstractyes

Explore more

Same venueEndangered Species ResearchSame topicTransportation Safety and Impact AnalysisFrench-language works237,207