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Record W2006046394 · doi:10.1071/mf07083

Contraction of the banana prawn (Penaeus merguiensis) fishery of Albatross Bay in the Gulf of Carpentaria, Australia

2008· article· en· W2006046394 on OpenAlexaff
J.D. Prince, Neil R. Loneragan, Thomas A. Okey

Bibliographic record

VenueMarine and Freshwater Research · 2008
Typearticle
Languageen
FieldEnvironmental Science
TopicMarine and fisheries research
Canadian institutionsBamfield Marine Sciences CentreUniversity of Victoria
Fundersnot available
KeywordsCarpentariaFisheryFishingPrawnBayPenaeusAlbatrossStock (firearms)Stock assessmentBiologyOceanographyGeographyShrimp

Abstract

fetched live from OpenAlex

When the biomass and area occupied by a stock decline together, catch rates can remain high (hyperstability) and management with effort controls may be ineffectual. Banana prawn (Penaeus merguiensis) catches declined from 2000 until 2005 in the Albatross Bay area in the Gulf of Carpentaria (GOC), Australia. Data from commercial logbooks were used to investigate historical changes in the banana prawn fishery in this and other regions of the Northern Prawn Fishery to infer the potential causes of this decline. Data since 1970 were analysed using: (1) the mapping of catch and effort; and (2) normalised rank order catch curves, to determine the distribution of catches across fishing areas. These analyses show that there has been a marked contraction of the Albatross Bay fishery over 33 years of fishing into the centre of a stable ‘hotspot’, suggesting a potential mechanism for the observed negative relationship between catchability and biomass. We believe this is the first observation of a Peneaus prawn fishing ground contracting as biomass declines, supporting the view that the contraction of an area occupied by a stock as biomass declines, is a generalised phenomena observed widely across fisheries resources and not a dynamic confined to certain finfish and molluscs.

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)0.002
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 score0.075
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0050.000

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 head0.066
GPT teacher head0.307
Teacher spread0.241 · 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

Citations7
Published2008
Admission routes1
Has abstractyes

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