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Record W4393921777 · doi:10.5281/zenodo.3246540

Avian community survey by point count method in a leafy forest of the Station de Biologie des Laurentides, Université de Montréal

2019· dataset· en· W4393921777 on OpenAlexaffabout
Virginie Lemieux‐Labonté, Cindy Bouchard

Bibliographic record

VenueZenodo (CERN European Organization for Nuclear Research) · 2019
Typedataset
Languageen
FieldEnvironmental Science
TopicEcology and Vegetation Dynamics Studies
Canadian institutionsUniversité de Montréal
Fundersnot available
KeywordsLeafyForestryGeographyBiologyHorticulture

Abstract

fetched live from OpenAlex

Here we provided a dataset of birds’ communities census in the Station de Biologie des Laurentides (SBL), the Université de Montréal field station. Census was performed as part of the BIO2854 field course. BIO2854 is an undergraduate course in terrestrial animal ecology in biological science at Université de Montréal. Highly qualified teaching assistant and students surveyed forest birds over 4 days in May-June 2011 to 2019 by acoustic point-count method. Abundance counts were made based on bird vital domain available in literature and the assumption that each recorded male was associated with one female from the same species. Data are made available to consult for future research and teaching purpose.

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)0.001
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.676
Threshold uncertainty score0.645

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.006
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0020.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0240.023

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 head0.032
GPT teacher head0.252
Teacher spread0.219 · 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 designNot applicable
Domainnot available
GenreDataset

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
Published2019
Admission routes2
Has abstractyes

Explore more

Same venueZenodo (CERN European Organization for Nuclear Research)→Same topicEcology and Vegetation Dynamics Studies→French-language works237,207→