MétaCan
Menu
Back to cohort
Record W4302176322 · doi:10.47886/9781934874110.ch18

Pacific Salmon: Ecology and Management of Western Alaska’s Populations

2009· book-chapter· en· W4302176322 on OpenAlexaboutno aff

Bibliographic record

VenueAmerican Fisheries Society eBooks · 2009
Typebook-chapter
Languageen
FieldHealth Professions
TopicIndigenous Studies and Ecology
Canadian institutionsnot available
Fundersnot available
KeywordsSubsistence agricultureFishingFisheryGeographyPer capitaAbaloneEcologyAgricultureBiologyPopulationArchaeology

Abstract

fetched live from OpenAlex

<em>Abstract.</em>—Salmon <em>Oncorhynchus </em>spp. is a staple food for the Native villages of the Yukon and Kuskokwim drainages and Norton Sound in Alaska. The economy of the area is characterized by the high production of wild foods for local use and low-per-capita monetary incomes. Traditional subsistence activities form the core of village economies. Subsistence harvests, the priority use of salmon designated by state and federal law, have displayed variable trends, primarily linked to local environmental variables and the food needs of people and sled dogs. Commercial fishing of western Alaska salmon stocks intensified during the early 1970s through 1980s, providing income to small-scale fishers selling to export markets. During the 1990s, commercial salmon harvests collapsed resulting in substantial decreases of income to villages. In the Yukon River drainage, families have culled dog teams in response to lower subsistence salmon harvests for dog food, impacting cultural traditions involving sled dogs. Declines in subsistence salmon harvests for food may lead to increased harvests of other wild-food species or cause human out-migration from villages; however, no programs are currently in place to monitor such effects. A growing number of case studies have documented the important contributions of Traditional Ecological Knowledge to fishery research as well as to the formulation of fisheries regulations.

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 categoriesMeta-epidemiology (narrow), Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.642
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0020.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.043
GPT teacher head0.315
Teacher spread0.272 · 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
GenreOther

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
Published2009
Admission routes1
Has abstractyes

Explore more

Same venueAmerican Fisheries Society eBooksSame topicIndigenous Studies and EcologyFrench-language works237,207