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Record W3095025955 · doi:10.1038/s41597-020-00711-y

Individual back-calculated size-at-age based on otoliths from Pacific coral reef fish species

2020· article· en· W3095025955 on OpenAlexaff
Fabien Morat, Jérémy Wicquart, Nina M. D. Schiettekatte, Guillemette De Sinéty, Jean Bienvenu, Jordan M. Casey, Simon J. Brandl, Jason Vii, Jérémy Carlot, Samuel Degregori, Alexandre Mercière, Pauline Fey, René Galzin, Yves Letourneur, Pierre Sasal, Valériano Parravicini

Bibliographic record

VenueScientific Data · 2020
Typearticle
Languageen
FieldEnvironmental Science
TopicMarine and fisheries research
Canadian institutionsSimon Fraser University
FundersNational Human Genome Research InstituteFondation de FranceFondation BNP ParibasBNP Paribas CardifAgence Nationale de la Recherche
KeywordsCoral reefCoral reef fishFisheryReefCoralFish <Actinopterygii>BiologyOceanographyGeographyEcologyGeology

Abstract

fetched live from OpenAlex

Somatic growth is a critical biological trait for organismal, population, and ecosystem-level processes. Due to its direct link with energetic demands, growth also represents an important parameter to estimate energy and nutrient fluxes. For marine fishes, growth rate information is most frequently derived from sagittal otoliths, and most of the available data stems from studies on temperate species that are targeted by commercial fisheries. Although the analysis of otoliths is a powerful tool to estimate individual growth, the time-consuming nature of otolith processing is one barrier for collection of comprehensive datasets across multiple species. This is especially true for coral reef fishes, which are extremely diverse. Here, we provide back-calculated size-at-age estimates (including measures of uncertainty) based on sagittal otoliths from 710 individuals belonging to 45 coral reef fish species from French Polynesia. In addition, we provide Von Bertalanffy growth parameters which are useful to predict community level biomass production.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.423
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0010.000
Open science0.0030.004
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.1480.004

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 head0.089
GPT teacher head0.264
Teacher spread0.175 · 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

Citations20
Published2020
Admission routes1
Has abstractyes

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