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Record W2101157423 · doi:10.14430/arctic4166

Stock Characteristics of Humpback Whitefish and Least Cisco in the Chatanika River, Alaska

2012· article· en· W2101157423 on OpenAlexvenueno aff
Trent M. Sutton, Lorena E. Edenfield

Bibliographic record

VenueARCTIC · 2012
Typearticle
Languageen
FieldEnvironmental Science
TopicFish Ecology and Management Studies
Canadian institutionsnot available
Fundersnot available
KeywordsCoregonusFisheryFishingFish measurementStock (firearms)PopulationStock assessmentOceanographyGeographyBiologyGeologyFish <Actinopterygii>

Abstract

fetched live from OpenAlex

Overharvest of humpback whitefish (Coregonus pidschian) and least cisco (C. sardinella) in the Chatanika River, Alaska, during the late 1980s led to collapsed stocks and closure of the fishery. We evaluated the stock characteristics of these two species to determine the extent of recovery. A total of 3207 humpback whitefish and 2766 least cisco were captured during their fall spawning migration in 2008. Humpback whitefish ranged from 188 to 583 mm in fork length (FL) and encompassed ages 5 to 29 years, while least cisco ranged from 215 to 425 mm in FL and their ages ranged from 3 to 14 years. Patterns in growth and length-at-age were similar for both species, and annual mortality rates were 31% for humpback whitefish (age 11 and older) and 44% for least cisco (age 9 and older). Population attributes were within the ranges observed for other North American stocks of humpback whitefish and least cisco. Although the humpback whitefish in the Chatanika River have stock attributes that are consistent with low exploitation and this species appears to have recovered, the least cisco in the river still exhibit many attributes that suggest the cisco stock has not fully recovered. The results of this study indicate that the current allowable harvest limit of 2000 whitefish is cautious and appears to be sustainable.

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)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.007
Threshold uncertainty score0.262

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)0.0000.000

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 head0.012
GPT teacher head0.212
Teacher spread0.200 · 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.

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

Citations2
Published2012
Admission routes1
Has abstractyes

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Same venueARCTICSame topicFish Ecology and Management StudiesFrench-language works237,207