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Record W2270460200

A reconstruction of the Ukraine's marine fisheries catches, 1950-2010

2015· article· en· W2270460200 on OpenAlexaff
Aylin Ulman, Vladyslav Shlyakhov, Daniel Pauly

Bibliographic record

VenueDergiPark (Istanbul University) · 2015
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicMarine Invertebrate Physiology and Ecology
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsDiscardsFisheryWhitingGeographySubsistence agricultureFisheries managementRecreational fishingHorse mackerelBycatchFishingFish <Actinopterygii>Biology
DOInot available

Abstract

fetched live from OpenAlex

AbstractUkraine’s marine fisheries catches were re-estimated for the 1950-2010 time period using a reconstruction approach which estimated all unreported fisheries removals, i.e., catches from the industrial, artisanal, recreational, and subsistence sectors, as well as discards from major fisheries. The reconstructed total catch for the 1950-2010 time period is 1.4 times the data we deemed officially reported on behalf of Ukraine to the FAO, which included only industrial landings. Reconstructed catches consisted to 71% of industrial, 11% artisanal, 8% recreational and 7% subsistence landings, while discards accounted for 3%. Total catches increased from about 50,000 tin 1950 to a peak of about 175,000 t in 1988, then declined with the collapse of the Soviet Union to about 55,000 t in 1991, also due to an invasion of ctenophores in the Black Sea. In 2010, total reported marine landings for Ukraine were about 70,000 t, while the reconstructed total catch was just over 110,000 t. Major unreported species were Mediterranean horse mackerel (Trachurus mediterraneus), gobies (Gobiidae), whiting (Merlangius merlangus), and bluefish (Pomatomus saltatrix). Accounting for all fisheries removals should help to establish a reliable baseline, better understand the fisheries, and thus assist management.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.123
Threshold uncertainty score0.999

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)0.0030.000

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 head0.018
GPT teacher head0.153
Teacher spread0.135 · 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.

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

Citations4
Published2015
Admission routes1
Has abstractyes

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