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Record W4403929785 · doi:10.1016/j.fishres.2024.107209

Implementing a fishery improvement programme to manage the common cuttlefish (Sepia officinalis) in artisanal sea and lagoon fisheries: The case study of the Chioggia’s fleet

2024· article· en· W4403929785 on OpenAlexaff
Emily Sepe, Federica Poli, Federico Calì, Simone D’Acunto, Carlotta Mazzoldi, Matteo Barbato

Bibliographic record

VenueFisheries Research · 2024
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicCephalopods and Marine Biology
Canadian institutionsDepartment of Environment and Conservation
FundersEuropean Maritime and Fisheries FundUniversità degli Studi di Padova
KeywordsSepiaCuttlefishFisheryFisheries scienceFishingOfficinalisFisheries managementBiology

Abstract

fetched live from OpenAlex

In the light of reducing the impact of fishery, artisanal fisheries hold great potential to achieve the sustainable use of marine resources if locally managed at sub-regional level (i.e., within the same province or fleet) and by implementing mitigation systems. The Chioggia small-scale fishing fleet represents a study case in an overexploited Mediterranean sub-region, operating both in the Venice Lagoon and the North Adriatic Sea. During the spawning period, this small-scale fishery targets the same target species, the common cuttlefish ( Sepia officinalis ), which, over the past thirty years, has shown a general decline in catch trends. Cuttlefish represent a high-value resource at all life stages, with both adults and juveniles considered targets of the artisanal fishery. Additionally, eggs are consistently laid on fishing gear during the breeding season and must be removed by fishers to prevent a reduction in fishing efficiency. These aspects contribute to the vulnerability of the common cuttlefish stocks, calling for a complementary management approach. We researched methods from the peer-reviewed literature, and adapted methods to reflect the environmental conditions of the sea and lagoon, and small-scale fishing methods. We combined into a complementary management approach: i) mitigation systems to reduce and collect eggs discarded during fishing operations, ii) semi-natural juvenile rearing diets to potentially release fishing pressure and support natural population, and iii) small-scale supply chain to both raise awareness about this resource vulnerability and promote the activity of fishers adopting virtuous behaviours. We tested these three functional management units in collaboration with lagoon and sea fishers and local stakeholders. We showed that the use of egg collectors, as an alternative spawning structure, performed well to limit the loss of the eggs laid upon the fishing gears. Second, we showed that Mixed and Natural diets contributed significantly to the growth rate of hatchlings compared to artificial diets. Finally, we demonstrated a shared interest in products derived from sustainable fisheries from the public, fishers and retailers. Therefore, the small-scale supply chain can represent a valid component to valorise the adoption of the whole management scheme. Overall, this complementary management approach can be adapted to and implemented in other local socio-economic communities of artisanal fishing in coastal environments worldwide. Complementary mitigation systems could work in synergy, overcoming the limitations of individual mitigation measures applied to diverse fishery resources.

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)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.041
Threshold uncertainty score0.082

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0010.000
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 head0.070
GPT teacher head0.331
Teacher spread0.260 · 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

Citations1
Published2024
Admission routes1
Has abstractyes

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