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Record W2056617566 · doi:10.1139/cjfas-2013-0331

Acoustic detection of mesopelagic fishes in scattering layers of the Balearic Sea (western Mediterranean)

2014· article· en· W2056617566 on OpenAlexvenueno aff
Marian Peña, M. Pilar Olivar, Rosa Balbín, José Luís López‐Jurado, M. Iglesias, Joan Miquel

Bibliographic record

VenueCanadian Journal of Fisheries and Aquatic Sciences · 2014
Typearticle
Languageen
FieldEnvironmental Science
TopicMarine animal studies overview
Canadian institutionsnot available
FundersMinisterio de Ciencia e Innovación
KeywordsMesopelagic zoneThermoclineWater columnOceanographyMediterranean seaHydrographyMediterranean climateBalearic islandsEnvironmental scienceMarine snowDiel vertical migrationWater massGeologyPelagic zoneEcologyBiology

Abstract

fetched live from OpenAlex

The distributions of micronekton layers in the Balearic Sea (western Mediterranean) were investigated by acoustic methods. Two multidisciplinary surveys were carried out in late autumn 2009 and summer 2010, recording acoustic, biological, and hydrographic data. We described acoustic layers, migratory behavior, sampled species, and water masses processes. Acoustic modeling of gas-bearing organisms was employed to explain differences between acoustic estimates and sampled abundances. The influence of environmental variables on the vertical distribution and migration pattern of these organisms was analyzed. The thermocline depth was related to the preferred depth for migrating myctophids, while nonmigrant species dwelled in the oxygen minimum zone of the water column both in late autumn and summer periods.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.039
Threshold uncertainty score0.078

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.021
GPT teacher head0.212
Teacher spread0.190 · 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".

Quick stats

Citations69
Published2014
Admission routes1
Has abstractyes

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