Has the pandemic (COVID-19) affected the fishery sector in regional scale? A case study on the fishery sector in Hatay province from Turkey
Bibliographic record
Abstract
Many sectors in all around the world including fisheries have been affected from COVID-19 pandemic. The aim of this study was to evaluate the early effects of the pandemic (COVID-19) on the fishery sector that have been conducting in regional scale considering the fishery sector in Hatay province from Turkey. A series of interviews were accomplished with the peoples/firms/enterprises belonging stakeholders (fishermen, retailer, wholesaler and exporters) to question the fishing effort and trade statistics covering the first quarter of 2019 and 2020. The most negative impact of the pandemic in terms of trade (in quantity, kg) was on the exporter with 65% decrease followed by wholesalers (35%), retailers (17% for fishing products and 14% aquaculture products). It was concluded that ERP (Enterprise Resource Planning) system should be constructed by the government to cope with the problems resulting from pandemics in the fisheries sectors. In this sense, within a decentralization aspect, local cooperatives would play an important role in setting a sector-oriented ERP.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
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".