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Record W2551695823

2007 Arctic Yukon Kuskokwim Sustainable Salmon Initiative Project Final Product Alternative methods for setting escapement goals in AYK 1

2007· article· en· W2551695823 on OpenAlexaboutno aff
Ray Hilborn, Robert B. Lessard, Dan Goodman, Brian G. Bue, Milo D. Adkison, Jack A. Stanford, Diane C. Whited, Eric Knudsen, Glacier Highway

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEnvironmental Science
TopicFish Ecology and Management Studies
Canadian institutionsnot available
Fundersnot available
KeywordsEscapementHydropowerComputer scienceStock (firearms)Environmental resource managementEnvironmental scienceOperations researchEngineeringGeography
DOInot available

Abstract

fetched live from OpenAlex

The escapement goals and management strategies for salmon stocks in the AYK region have been the subject of considerable controversy yet are critical in the management of these resources. It is widely recognized that there are limitations to the existing methods of creating brood tables and fitting Ricker or other stock-recruitment curves to these data, given the limited information for many AYK systems. In recent years there have been a number of new initiatives for evaluation of escapement goals, including methods that formally incorporate uncertainty and risk, habitat conditions, explicit analysis of life histories, use of data other than brood tables, understanding of stock structure and biocomplexity within watersheds, and evaluation of objectives other than maximum sustained yield. This project brought together a range of experts to evaluate the utility of these new methods for determining escapement goals for AYK stocks, assemble existing data relevant to calculation of AYK escapement goals, and to try to apply the new tools to several AYK systems. In this report, we present results from six activities. First, we provide a summary of existing data potentially useful for traditional escapement goal analyses, and a listing of all the technical reports in support of those data. In addition, CDs of the actual data are being supplied to AYK SSI staff. Second, we present a preliminary approach for assigning a metric to data quality so that data quality might be incorporated into future models. Third, we report on the reanalysis of productivity changes in three AYK salmon stocks, including three additional years of brood-year catch and escapement data. The reanalysis reinforced the conclusions from the 2004 analysis: all three stocks exhibit a strong downward, long-term trend in density-corrected recruits per spawner; and the strong year effects after correcting for both the trend and density show strong driving by some regional factor that affects all three stocks more or less synchronously. Fourth, we present a brief discussion of the potential effects of marine-derived nutrients on escapement goals. Fifth, we compare a new method of life-history-based modeling to outcomes of traditional spawner-recruit models. We find that escapement goals as determined by a life history approach are lower than escapement goals using a traditional Ricker analysis. Last, we present a preliminary overview of the kind of analysis that could eventually be useful for habitat-based estimates of spawning goals, by linking the total estimated rearing area of different types to the production rates for each habitat type.

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)0.007
metaresearch head score (Gemma)0.007
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.187
Threshold uncertainty score0.372

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.007
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.003
Science and technology studies0.0010.001
Scholarly communication0.0030.001
Open science0.0010.002
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0210.005

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 head0.039
GPT teacher head0.360
Teacher spread0.322 · 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 designNot applicable
Domainnot available
GenreMethods

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

Citations2
Published2007
Admission routes1
Has abstractyes

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