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Végétalisons nos cours d’eau : les ripisylves, un habitat aux multiples bénéfices

2025· article· fr· W4416602437 on OpenAlexaff
Anthony Maire, Adeline AIRD, Marylise Cottet, Camille DEBEIN, Léa Dieckhoff, Simon Dufour, Martial DURBEC, André Évette, Sabine Greulich, Ines Imbert, Philippe Janssen, Marion Legrand, Charlotte LE MOIGNE, Baptiste Marteau, Florentina Moatar, Hervé Piégay, Nicolas Poulet, Laura RODRIGUEZ, Hanieh Seyedhashemi, Laurence Tissot, Anne VIVIER, Cybill Staentzel

Bibliographic record

VenueSciences Eaux & Territoires · 2025
Typearticle
Languagefr
FieldEnvironmental Science
TopicHydrology and Sediment Transport Processes
Canadian institutionsShipping Federation of Canada
Fundersnot available
KeywordsWestern europeElectrocutionViewshed analysis

Abstract

fetched live from OpenAlex

La ripisylve désigne l’ensemble des peuplements forestiers et boisements linéaires situés aux abords des cours d'eau, à l’interface entre les milieux aquatique et terrestre. Ces dernières décennies, un nombre croissant d’études scientifiques ont mis en lumière les intérêts écologiques, sociétaux et économiques de la préservation et de la restauration des ripisylves. Dans la première partie de cet article, nous synthétisons les bénéfices associés aux ripisylves. Nous présentons, dans une seconde partie, les éléments soutenant la préservation et la restauration des ripisylves comme des stratégies d’atténuation du changement climatique et d'adaptation à ses effets sur les rivières, la biodiversité et les humains. Ces caractéristiques font de la restauration de la végétation des berges des cours d’eau une mesure de gestion à développer dans les années à venir, dans un cadre réfléchi et adapté aux contraintes locales.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.018

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0010.001
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 head0.017
GPT teacher head0.268
Teacher spread0.251 · 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".

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Citations0
Published2025
Admission routes1
Has abstractyes

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