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Record W1997174146 · doi:10.2980/18-4-3401

Exploratory analysis of correlates of the abundance of rusty blackbirds (<i>Euphagus carolinus</i>) during fall migration

2011· article· en· W1997174146 on OpenAlexaffvenueabout
Jean‐Pierre L. Savard, Mélanie Cousineau, Bruno Drolet

Bibliographic record

VenueEcoscience · 2011
Typearticle
Languageen
FieldEnvironmental Science
TopicClimate variability and models
Canadian institutionsEnvironment and Climate Change Canada
Fundersnot available
KeywordsBorealAbundance (ecology)EcologyPopulationTaigaEstuaryShorePrecipitationGeographyBiologyPhysical geographyDemographyFishery

Abstract

fetched live from OpenAlex

The rusty blackbird (Euphagus carolinus) has received much attention during the last decade, in part due to drastic population declines. We analyzed data from 15 y of fall migration monitoring at the Observatoire d'oiseaux de Tadoussac (OOT), located at the mouth of the Saguenay River on the north shore of the St. Lawrence River estuary in Quebec, Canada. The trend observed suggest an ongoing decline. Numbers of rusty blackbirds varied considerably between years, with peak movements occurring at 5-y intervals and possibly reflecting high reproductive success for those years. The numbers of adult boreal owls caught and banded at the OOT, and the proportion of juveniles, were negatively and positively correlated with rusty blackbird numbers, respectively. Peaks in rusty blackbird abundance occurred when red-backed voles (Clethrionomys gapperi) were abundant in the eastern boreal forest. Also, rusty blackbird numbers were positively correlated to the annual and winter North Atlantic Oscillation (NAO) indices and negatively to the combined precipitation for June, July, and August, suggesting that environmental factors may have contributed both directly and indirectly (through food web processes) to the cyclic variations in abundance observed. Current declines may be exacerbated by NAO fluctuation patterns, that is, more frequent negative indices may negatively affect reproductive success and possibly winter survival.

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 score0.036
Threshold uncertainty score0.318

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.020
GPT teacher head0.208
Teacher spread0.188 · 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

Citations9
Published2011
Admission routes3
Has abstractyes

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