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Record W3010348588 · doi:10.21199/wb51.1.4

EARLIER SPRING ARRIVAL OF THE MOUNTAIN BLUEBIRD IN CENTRAL ALBERTA, CANADA

2020· article· en· W3010348588 on OpenAlexaboutno aff
Myrna Pearman, Leo de Groot, Geoffrey L. Holroyd, Stephanie Thunberg

Bibliographic record

VenueWestern Birds · 2020
Typearticle
Languageen
FieldEnvironmental Science
TopicFire effects on ecosystems
Canadian institutionsnot available
Fundersnot available
KeywordsSpring (device)GeographyArchaeologyPhysical geographyEngineering

Abstract

fetched live from OpenAlex

Much attention has been given to the topic of bird phenology in response to climate change. While strong evidence supports a general pattern of advancement in spring migrants’ arrival dates with warming temperatures, the mechanisms underlying these changes are not clearly understood. We summarize the spring arrival of the Mountain Bluebird (Sialia currucoides) in central alberta from 58 years of data and examine the inflence of temperature and snow cover on the patterns of arrival. We hypothesized that a signifiant advance in the Mountain Bluebird’s fist arrival date was related to weather variables. in central alberta, March temperatures increased, and fist arrival dates for the Mountain Bluebird advanced0.33 days per year from 1961 to 2018 or 19 days over the 58 years. However, temperatures on the date of arrival have cooled slightly (2.8 °C) over the study period, and snow depth on the date of arrival decreased slightly (1.5 cm) over the study period, which may inflence early migrants’ opportunities for foraging. although Mountain Bluebirds have arrived at our central alberta study area considerably earlier over the past decades, temperatures and snow depth have been highly variable, suggesting that the species is likely responding to multiple cues that inflence its arrival dates. Given the Mountain Bluebird’s migratory nature, environmental and behavioral stimuli en route to breeding areas likely exert considerable inflence on arrival dates.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.041
Threshold uncertainty score0.347

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 head0.006
GPT teacher head0.178
Teacher spread0.173 · 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
Published2020
Admission routes1
Has abstractyes

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