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Record W1558984717 · doi:10.1111/cobi.12276

Shift in Trophic Level of Mediterranean Mariculture Species

2014· article· en· W1558984717 on OpenAlexaff
Athanassios C. Tsikliras, Konstantinos I. Stergiou, Nikolaos D. Adamopoulos, Daniel Pauly, Eleni Mente

Bibliographic record

VenueConservation Biology · 2014
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAquaculture Nutrition and Growth
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsMaricultureTrophic levelFisherySparidaeFish farmingBiologyDicentrarchusMediterranean climateSpratFish mealPagrus majorAquacultureEcologyFish <Actinopterygii>Herring

Abstract

fetched live from OpenAlex

The mean trophic level of the farmed fish species in the Mediterranean has been increasing. We examined the farming-up hypothesis (i.e., the increase in the production of high-trophic-level species) in the Mediterranean by determining the trophic level of the aquafeeds (i.e., what the fish are fed) of 5 species of farmed marine fishes: common dentex (Dentex dentex), common pandora (Pagellus erythrinus), European seabass (Dicentrarchus labrax), gilthead seabream (Sparus aurata), and red porgy (Pagrus sp.). The mean trophic level of aquafeed used in mariculture from 1950 to 2011 was higher (3.93) than the prey farmed fish consume in the wild (3.72) and increased at a faster rate (0.48/decade) compared with that based on their diets in the wild (0.43/decade). Future expected replacement of the fishmeal and oil in aquafeeds by plant materials may reverse the farming-up trend, although there are a number of concerns regarding operational, nutritional, environmental, and economic issues. The farming-up reversal can be achieved in an ecologically friendly manner by facilitating the mariculture of low-trophic-level fishes and by promoting high efficiency in the use of living marine resources in aquafeeds.

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)0.000
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.684
Threshold uncertainty score0.428

Codex and Gemma teacher scores by category

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

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 head0.100
GPT teacher head0.256
Teacher spread0.156 · 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

Citations13
Published2014
Admission routes1
Has abstractyes

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