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Record W3110885754 · doi:10.3389/fmars.2020.573986

Spatial Distribution and Abundance of Mesopelagic Fish Biomass in the Mediterranean Sea

2020· article· en· W3110885754 on OpenAlexafffund
Morane Clavel‐Henry, Chiara Piroddi, Federico Quattrocchi, Diego Macías, Villy Christensen

Bibliographic record

VenueFrontiers in Marine Science · 2020
Typearticle
Languageen
FieldEnvironmental Science
TopicMarine and fisheries research
Canadian institutionsUniversity of British ColumbiaFisheries and Oceans Canada
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsMesopelagic zoneBiomass (ecology)Environmental scienceSpatial distributionAbundance (ecology)OceanographyMediterranean seaMediterranean climateFisheryEcologyBiologyGeographyPelagic zoneGeologyRemote sensing

Abstract

fetched live from OpenAlex

Mesopelagic fish, being in the middle of the trophic web, are important key species for the marine environment; yet limited knowledge exists about their biology and abundance. This is particularly true in the Mediterranean Sea where no regional assessment is currently undertaken regarding their biomass and/or distribution. This study evaluates spatial and temporal patterns of mesopelagic fish biomass in the 1994–2011 period. We do that for the whole Mediterranean Sea using two well-established statistical models, the Generalized Additive Model (GAM) and Random Forest (RF). Results indicate that the bathymetry played an important role in the estimation of mesopelagic fish biomass and in its temporal and spatial distribution. The average biomass over the whole time period reached 1.08 and 0.10 t/km 2 , depending on the model considered. The Western Mediterranean and Ionian Seas were the sub-regions with the highest biomass, while the Adriatic was the area with the lowest. Temporal trends showed different trajectories with steep decrease and a fluctuation, using respectively RF and GAM. This study constitutes the first attempt to estimate the biomass and the spatial temporal patterns of mesopelagic fish using environmental variables as predictors. Given the growing interest in mesopelagic fish, our study sets a baseline to further develop mesopelagic fish biomass assessments in the region. Our results stress the need to improve data collection and quality in the region while identifying appropriate tools to better understand and assess the processes behind mesopelagic fish dynamics in the basin.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.074
Threshold uncertainty score0.506

Codex and Gemma teacher scores by category

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

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 head0.013
GPT teacher head0.232
Teacher spread0.219 · 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 teacher head, 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

Citations23
Published2020
Admission routes2
Has abstractyes

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