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Maximum economic yield in crisis?

2010· article· en· W2112548472 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueFish and Fisheries · 2010
Typearticle
Languageen
FieldEnvironmental Science
TopicSustainable Development and Environmental Policy
Canadian institutionsUniversity of British Columbia
FundersMinisterstvo Školství, Mládeže a TělovýchovyPew Charitable Trusts
KeywordsRecessionMaximum sustainable yieldYield (engineering)EconomicsValue (mathematics)Natural resource economicsSustainabilityFishingMultiplier (economics)Sustainable yieldMicroeconomicsFisheries managementFisheryMacroeconomicsMathematicsStatisticsEcology

Abstract

fetched live from OpenAlex

Abstract We examine the claim in Christensen that Maximum Economic Yield (MEY) is equal to Maximum Sustainable Yield (MSY). The basis for this claim is that MEY considers only the ‘catching’ of fish and that when the full value‐chain is considered; it is the MSY level that maximizes economic value. We argue that to maximize society’s benefit from a given sector of an economy, resources need to be allocated across all sectors such that additional net benefits from employing one more unit of society’s resources are equalized across all sectors of the economy. In this way, the opportunity cost of employing society’s resources across all economic sectors is minimized. In an economy where all resources are fully utilized, further value added in the value chain for fish is an additional cost and has the effect of reducing fishing effort and optimum yield rather than the opposite. In a less developed economy or a developed one in recession where all resources are not fully used, the multiplier effect could be important, and if it is high for fisheries it would be an argument to maximize sustainable yield and effort. We show, using current input‐output data, that this is not the case. Furthermore, from a simple principle of optimization, we know that to optimize a sector that consists of many segments through time, one has to optimize every portion of the chain through time.

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.

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 score0.110
Threshold uncertainty score0.994

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)0.0070.000

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 head0.007
GPT teacher head0.184
Teacher spread0.177 · 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