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Record W4221120514 · doi:10.3390/jmse10030387

The Dynamics of Maximum Lengths for the Invasive Silver-Cheeked Toadfish (Lagocephalus sceleratus) in the Eastern Mediterranean Sea

2022· article· en· W4221120514 on OpenAlexaff
Aylin Ulman, Stefanos Kalogirou, Daniel Pauly

Bibliographic record

VenueJournal of Marine Science and Engineering · 2022
Typearticle
Languageen
FieldEnvironmental Science
TopicMarine Ecology and Invasive Species
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsMediterranean seaMediterranean climateRange (aeronautics)FisheryOceanographyMediterranean BasinToadfishBiologyGeographyEcologyFish <Actinopterygii>Geology

Abstract

fetched live from OpenAlex

The Eastern Mediterranean Sea is the most invaded sea on the planet, with 666 non-indigenous species now recorded in the region. However, not all of these become successful in their new environments. Success here is defined by wide geographical spread, increased abundances, and larger maximum sizes than their native range. The silver-cheeked toadfish Lagocephalus sceleratus (Gmelin 1789) was first recorded in the Mediterranean Sea in 2003. It has now spread to all corners of the basin and is increasingly abundant in the Eastern Mediterranean Sea where it reaches monstrous sizes compared to the maximum sizes reported from its native range. This contribution presents three well-documented new weight records from the Dodecanese Islands, Greece: one specimen weighing 8.5 kg from 2012, and two specimens weighing 8 and 9 kg, respectively, from 2021. The latter is also confirmed with other well-documented larger-size records, along with a physiological hypothesis suggesting how such large sizes are reached.

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)0.001
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 score0.006
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
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 head0.011
GPT teacher head0.208
Teacher spread0.197 · 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

Citations12
Published2022
Admission routes1
Has abstractyes

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