MétaCan
Menu
Back to cohort
Record W6996982797

They're staying how long? Methods of and complications in determining stopover estimates using banding data

2024· article· en· W6996982797 on OpenAlexaboutno aff

Bibliographic record

VenueDigital Commons - University of South Florida (University of South Florida) · 2024
Typearticle
Languageen
FieldDecision Sciences
TopicAdvanced Statistical Process Monitoring
Canadian institutionsnot available
Fundersnot available
KeywordsNucleofectionGestational periodTSG101DysgeusiaLiquationDiafiltrationTriacetinEmperipolesisDemotion
DOInot available

Abstract

fetched live from OpenAlex

We banded 4,034 nestlings in 1,433 successful Ferruginous Hawk (Buteo regafis) nests in Saskatchewan between 1969 and 2005.The unexplained but sudden and prolonged drop in ground squirrel numbers, 1987 -1996, had a less detrimental effect on Ferruginous Hawk productivity in grassland regions over ten consecutive years than was experienced by the Swainsoh's Hawk (Buteo swainsoni; Houston and Zazelenchuk 2004, Houston 2005).METHODS Since 1969, we have concentrated on banding Ferruginous Hawks on and near nine large Prairie Farm Rehabilitation Administration (PFRA) pastures in west-central Saskatchewan between RosetoWn and the Alberta boundary.These pastures host beef cattle and are without feed lots A map of the main banding area, with plots of percent natural grassland remaining, can be found in Schmutz et al. (2001).Our study area was not completely searched and had no well-defined boundaries.Over the years, we have increased search and banding efforts with the help of pasture managers and local resident birdwatchers.Records of ground squirrel numbers in western Canada are close to non-existent.Our visual, somewhat anecdotal, observations of ground squirrel abundance and their inverse relation to fox North American Bird Bander Vol. 30 No 4Ferruginous Hawk Productivity In Saskatchewan, 1969Saskatchewan, -2004

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.024
metaresearch head score (Gemma)0.062
Version: metacan-v3-hybrid-931329e0061cValidation 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: none
Teacher disagreement score0.033
Threshold uncertainty score0.126

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0240.062
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.003
Science and technology studies0.0010.001
Scholarly communication0.0020.002
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.244
GPT teacher head0.396
Teacher spread0.152 · 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 source (direct Gemma or distilled Codex), 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
Published2024
Admission routes1
Has abstractyes

Explore more

Same venueDigital Commons - University of South Florida (University of South Florida)Same topicAdvanced Statistical Process MonitoringFrench-language works237,207