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Record W1597782331

Harlequins Histrionicus histrionicus in a Rocky Mountain watershed I: Background and general breeding ecology

2000· article· en· W1597782331 on OpenAlexaboutno aff
Bill Hunt, Ron Ydenberg

Bibliographic record

VenueWildfowl (Wildfowl & Wetlands Trust) · 2000
Typearticle
Languageen
FieldEnvironmental Science
TopicAvian ecology and behavior
Canadian institutionsnot available
Fundersnot available
KeywordsTributaryWatershedNest (protein structural motif)GeographySTREAMSEcologyFisheryBiology
DOInot available

Abstract

fetched live from OpenAlex

The Maligne Valley, a watershed draining into the Athabasca River in the Rocky Mountains, in Jasper National Park, Canada, is a breeding area for Harlequin Ducks (Histrionicus histrionicus). Based on peak counts, some 30 - 40 adults enter the valley each spring, arriving in early May along the Athabasca River. Numbers build steadily in the valley until the period of peak flow, and individuals are highly faithful to particular sections of the main watercourse. Feeding is intensive prior to nest initiation. Harlequins use lakes, outlets, rivers, and tributaries in the valley in a variety of ways. Along the Lower Maligne River, a few Harlequin pairs defend territories, but the majority of birds feed in aggregations at major lake outlets and inlets, likely highly productive places. On Maligne Lake birds feed in scattered pairs, generally situated at stream inlets. Females begin nesting in mid-June following peak flow, and males depart the valley shortly thereafter. Nests are placed along both the main course of the Maligne River and along several tributaries, but the upper and lower sections of the Maligne River accounted for 11 of the 14 broods located. Many females move their broods to two large lakes for rearing.

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)0.000
metaresearch head score (Gemma)0.000
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: Empirical
Teacher disagreement score0.052
Threshold uncertainty score0.103

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.013
GPT teacher head0.233
Teacher spread0.220 · 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

Citations3
Published2000
Admission routes1
Has abstractyes

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