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Record W3202251394 · doi:10.1007/s11160-021-09685-5

Initial recovery of demersal fish communities in coastal waters of Hong Kong, South China, following a trawl ban

2021· article· en· W3202251394 on OpenAlexaff
Yanny K. Y. Mak, Lily S. R. Tao, Valerie C. M. Ho, David Dudgeon, William W. L. Cheung, Kmy Leung

Bibliographic record

VenueReviews in Fish Biology and Fisheries · 2021
Typearticle
Languageen
FieldEnvironmental Science
TopicMarine and fisheries research
Canadian institutionsUniversity of British Columbia
FundersResearch Grants Council, University Grants Committee
KeywordsTrawlingFisheryFishingDemersal zoneBottom trawlingGeographyTrophic levelEstuaryDemersal fishHabitatEcologyBiology

Abstract

fetched live from OpenAlex

Abstract Fisheries resources in Hong Kong have been overexploited since the 1970s due to intensive bottom trawling and other fishing activities that have depleted stocks and destroyed marine habitat. To rehabilitate depleted fisheries resources, a permanent ban on trawling in Hong Kong territorial waters came into force on December 31, 2012. In order to determine whether the trawl facilitated recovery of fish communities, trawl surveys were conducted at two sites in each of the eastern, southern and western (estuarine) coastal waters of Hong Kong before and three years after the trawl ban. A total of 315 species and 86 families of fishes in nine feeding groups were encountered during the surveys. Mean trophic level of the fish community, abundance and biomass of total fishes and of predatory fishes increased in eastern and western waters after the ban, but no changes or declines in these metrics were observed in southern waters. Although initial recovery in fish community were observed in eastern and western waters, anthropogenic disturbances might hinder the recovery process, including a large-scale reclamation for construction of coastal infrastructures in the west, illegal trawling, and expansion of non-trawling fishing efforts in the southern and eastern waters. Longer term monitoring is needed to evaluate the effects of the trawl ban, and determine whether recovery in the southern waters will continue to be constrained by the anthropogenic disturbances. Graphic abstract

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)0.000
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 score0.197
Threshold uncertainty score0.392

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
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 head0.033
GPT teacher head0.282
Teacher spread0.250 · 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

Citations22
Published2021
Admission routes1
Has abstractyes

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