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Record W2974842986

GCFI versus global fisheries: marine ecosystems, food security and the data connection

2008· article· en· W2974842986 on OpenAlexaffabout
Dirk Zeller

Bibliographic record

VenueAquaDocs (United Nations Educational, Scientific and Cultural Organization) · 2008
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicFisheries and Aquaculture Studies
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsFood securityFisheryEcosystemConnection (principal bundle)Marine fisheriesMarine ecosystemEnvironmental scienceEnvironmental resource managementBusinessNatural resource economicsFishingEcologyEconomicsBiologyAgricultureMathematics
DOInot available

Abstract

fetched live from OpenAlex

Globally, the only source of fisheries catch data is the global landings database maintained by the FAO of the United Nations, which is mandated to rely on member countries' reported data.However, these data are known to be incomplete, as they generally focus on commercial and/or export fisheries, and report only poorly or not at all on all other fisheries sectors, including all non-commercial catches, such as subsistence or recreational fisheries, or illegal fisheries (Pauly and Zeller 2003).Yet, FAO data are used for regional and global status assessments.The Sea Around Us Project (Pauly 2007) has, as one of its projects, taken on the task of reconstructing total catches (in contrast to reported landings) per country.The first outcome of this was the realisation that China had over-reported its catches (Watson and Pauly 2001), which, once corrected for, given the magnitude of China's contribution to global catches, resulted in a clear demonstration that global landings had peaked in the late 1980s and appears to have been declining since.Subsequent work by us has shown that under-reporting of catches to FAO by most other countries, especially from small-scale fisheries, is so strong as to often invalidate inferences based on official figures (Zeller et al. 2006, Zeller et al. 2007, Zeller and Pauly 2007).This problem is even more dramatic in the arctic region spanning Canada, Alaska and the Siberian coast of Russia, from which, despite heavy reliance on hunting and fishing by the local population, zero catch is reported to FAO by these well developed countries (Booth and Watts 2007, Pauly and Swartz 2007, Booth and Zeller 2008).And the Caribbean region is not exempt from this problem (Baisre et al. 2003, Mendoza et al. 2003).Despite such fundamental data problems, fisheries resources play a crucial food security role in many countries, in particular so in developing countries (Jacquet 2009), including much of the Caribbean region.However, the current, 'developed country' style fisheries science and management, which traditionally has been 'exported' (generally with good intentions) to developing countries either implicitly as part of 'development projects', or indirectly through overseas training of local scientists and managers, is generally disconnected with respect to both ecosystem-based management needs and developing countries financial and human resource options.The solution lies in moving rapidly towards comprehensive approaches that do not rely on data-heavy methods first pioneered in data-rich and wealthy Europe and North America.This includes increased emphasis on spatial management, ocean zoning and the establishment and enforcement of no-take areas, which are now known to have strong potential to maintain and enhance long-term fisheries yield (despite some shortterm costs).Creating protected areas can also have the additional benefit of creating alternative livelihoods, such as those related to tourism.As far as individual countries and their governments are concerned, emphasis also has to shift away from a 'development' and 'commercial' profit oriented fisheries focus, to a more social and democratic focus that deals in the first instance with local, national and regional food security issues, well before profit margins.This has added urgency for developing countries, given the projected strong negative effects of climate change on local biodiversity and fisheries potential in the low latitudes (Cheung et al.In press-a, Cheung et al.In press-b).This can only be achieved by focusing on local small-scale fisheries (Pauly 2006), and being extremely cautious about any foreign access-based fishing in a country's EEZ waters, which have been shown to be only at the detriment and expense of the host country's resource security and livelihoods (Kaczynski and Fluharty 2002, Jacquet and Zeller 2007a, 2007b).

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.437
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.006
Science and technology studies0.0040.001
Scholarly communication0.0000.001
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 head0.036
GPT teacher head0.237
Teacher spread0.201 · 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 designNot applicable
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

Citations0
Published2008
Admission routes2
Has abstractyes

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