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Les fascines de ligneux

2023· article· fr· W4387529671 on OpenAlexaff
Marie Didier, André Evette, Emma Schmitt, Solange Leblois, Delphine Jaymond, Jean-Baptiste Evette, Eléonore Mira, Pierre Raymond, Pierre-André Frossard, Anne VIVIER

Bibliographic record

VenueSciences Eaux & Territoires · 2023
Typearticle
Languagefr
FieldEnvironmental Science
TopicHydrology and Sediment Transport Processes
Canadian institutionsTerragon Environmental Technologies (Canada)
Fundersnot available
KeywordsGeographyForestryHumanitiesArt

Abstract

fetched live from OpenAlex

Représentant près d'un quart des ouvrages de génie végétal pour le contrôle de l'érosion en rivière en France, les fascines de ligneux sont une méthode privilégiée pour la stabilisation. Ces techniques ont une origine ancestrale et sont mises en œuvre de manière variée de nos jours, en fonction des objectifs spécifiques et des contextes. Que ce soit pour la diversification des habitats, le drainage, ou encore la protection des talus et des berges, le fascinage offre une grande flexibilité d'application.

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)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.016

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0050.001

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 head0.028
GPT teacher head0.281
Teacher spread0.253 · 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

Citations2
Published2023
Admission routes1
Has abstractyes

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