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Levelling-up rhodolith-bed science to address global-scale conservation challenges

2023· article· en· W4380319861 on OpenAlexaff
Fernando Tuya, Nadine Schubert, Julio Aguirre, Daniela Basso, Eduardo Bastos, Flávio Augusto de Souza Berchez, Ângelo F. Bernardino, Néstor E. Bosch, Heidi L. Burdett, Fernando Espino, Cindy Fernández‐García, Ronaldo B. Francini‐Filho, Patrick Gagnon, Jason M. Hall‐Spencer, Ricardo Haroun, Laurie C. Hofmann, Paulo Antunes Horta, Nicholas A. Kamenos, Line Le Gall, Rafael A. Magris, Sophie Martin, Wendy A. Nelson, Pedro Neves, Irene Olivé, Francisco Otero‐Ferrer, Viviana Peña, Guilherme H. Pereira‐Filho, Federica Ragazzola, Ana Cristina Rebelo, Cláudia Ribeiro, Eli Rinde, Kathryn M. Schoenrock, João Silva, Marina Nasri Sissini, Frederico Tapajós de Souza Tâmega

Bibliographic record

VenueThe Science of The Total Environment · 2023
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicMarine and coastal plant biology
Canadian institutionsMemorial University of Newfoundland
FundersJapan Society for the Promotion of ScienceAgencia Nacional de Investigación y DesarrolloFundação para a Ciência e a TecnologiaStazione Zoologica Anton DohrnBundesamt für LandwirtschaftBundesministerium für Ernährung und LandwirtschaftFinanciadora de Estudos e ProjetosFundação de Amparo à Pesquisa e Inovação do Estado de Santa CatarinaConselho Nacional de Desenvolvimento Científico e TecnológicoUniversity of TsukubaCoordenação de Aperfeiçoamento de Pessoal de Nível Superior
KeywordsEcosystem servicesSustainabilityBiodiversityClimate changeEnvironmental resource managementCoral reefHabitatMarine protected areaEcosystemGeographyEnvironmental planningEcologyEnvironmental scienceBiology

Abstract

fetched live from OpenAlex
No abstract in any covered source. Its absence is recorded, not treated as a negative.

No abstract. This is not a gap in this database; OpenAlex has none either. 23.3% of the frame is in this state, and the screen finds HALF as much metaresearch here, so the absence is a measured bias rather than a missing field.

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)0.011
metaresearch head score (Gemma)0.009
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.021
Threshold uncertainty score0.069

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0110.009
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0020.004
Scholarly communication0.0060.007
Open science0.0030.009
Research integrity0.0030.005
Insufficient payload (model declined to judge)0.0210.004

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 head0.230
Teacher spread0.189 · 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 designTheoretical or conceptual
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

Citations75
Published2023
Admission routes1
Has abstractno

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