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Record W2921274145 · doi:10.1051/nss/2019009

L’interdisciplinarité en pratique : retour d’expérience de la deuxième école d’été australe sur la vulnérabilité du patrimoine récifal (EEA VulPaRe 2016)

2018· article· fr· W2921274145 on OpenAlexfundno aff
Bertrand Morandi, Francesca Marin, Isabel Urbina‐Barreto, Adrien Comte, Romeo Brice Kolawolé Chabi, Faustinato Behivoke, Mirhani Nourddine, Mickael Uger, Silvia Galuppi, Benjamin Bandeira, E. Delvaux, Gasimandova Lahitsiresy Max, Jean-Jacques Manahirana, Landry Moma, Nassur Ahamada Mroimana, Ahmed Nassuf, Julie Pereira, Fidèle Rakotojanahary, José Randrianandrasana, Nadiée Rasolontiavina, Séraphin Remisy

Bibliographic record

VenueNatures Sciences Sociétés · 2018
Typearticle
Languagefr
FieldEnvironmental Science
TopicCoral and Marine Ecosystems Studies
Canadian institutionsnot available
FundersUniversité de MontpellierUniversité de Bretagne OccidentaleAgence Universitaire de la FrancophonieUniversité de La RéunionInstitut de Recherche pour le Développement
KeywordsHumanitiesPolitical scienceArt

Abstract

fetched live from OpenAlex

En novembre 2016, s’est tenue à Toliara, dans le sud-ouest de Madagascar, la deuxième école d’été australe sur la vulnérabilité du patrimoine récifal (EEA VulPaRe). Coorganisée par l’IRD (Institut de recherche pour le développement, France) et l’IHSM (Institut halieutique et des sciences marines de l’Université de Toliara, Madagascar), cette formation a proposé une approche interdisciplinaire de la thématique des récifs coralliens. Elle a été dispensée sous la forme de cours magistraux, de pratiques de terrain et de débats portant sur les questions environnementales, de connaissance, de valorisation et de conservation de ces milieux. Les participants de l’EEA VulPaRe 2016 souhaitent, à travers le présent article, donner à la communauté scientifique un retour d’expérience critique sur une démarche originale de formation à la recherche interdisciplinaire.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesScience and technology studies
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.100
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0020.006
Scholarly communication0.0010.002
Open science0.0020.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.000

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 head0.010
GPT teacher head0.319
Teacher spread0.309 · 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; both teacher heads agree on what is shown here.

Study designNot applicable
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
Published2018
Admission routes1
Has abstractyes

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