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Record W6892014440 · doi:10.48579/pro/9si1pn

Vegetation surveys in mountainous area (Pyrenees, France)

2022· dataset· en· W6892014440 on OpenAlexaff

Bibliographic record

Venuedata.InDoRES · 2022
Typedataset
Languageen
Field
Topic
Canadian institutionsCanadian Nautical Research Society
Fundersnot available
KeywordsVegetation (pathology)Cover (algebra)Vegetation coverRaw dataLand coverScale (ratio)Data fileSnow cover

Abstract

fetched live from OpenAlex

The data file contains absolute cover percentage of plant species from vegetation survey in the Vicdessos mountainous area (Pyrenees, France). The data have been collected in 2015 and 2020 within the framework of two projects: ANR-10-JCJC-1804 MODE-RESPYR (Modeling Past and future land cover changes in the Pyrenees) and the PASTSERV project funded by the Observatoire Hommes-Milieux Pyrenees Haut Vicdessos (Labex DRIIHM ANR-11-LABX0010). All observed vascular plant species were listed and the cover of each species was estimated using the seven degrees of the Braun-Blanquet scale (r, +, 1, 2, 3, 4, 5). These codes were then converted to absolute percentage cover (van der Maarel 1979). The version of the data file formatted according to the Darwin Core standard has been uploaded to GBIF (https://doi.org/10.15468/4q47d3). This file is the original raw data file.

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)0.003
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 score0.096
Threshold uncertainty score0.190

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
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 science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0530.030

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 head0.041
GPT teacher head0.298
Teacher spread0.257 · 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
Published2022
Admission routes1
Has abstractyes

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