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Record W6998062289

内陸アラスカ・クスコクィム川上流域におけるサケ漁撈史と現代的課題

2019· article· en· W6998062289 on OpenAlexaboutno aff

Bibliographic record

VenueHokkaido University Collection of Scholarly and Academic Papers (Hokkaido University) · 2019
Typearticle
Languageen
FieldEnvironmental Science
TopicEcology and biodiversity studies
Canadian institutionsnot available
Fundersnot available
KeywordsFishingSubsistence agricultureIndigenousBeaverFish <Actinopterygii>WildlifeArtisanal fishing
DOInot available

Abstract

fetched live from OpenAlex

In this paper, I describe a history of indigenous salmon fishing technologies and management issues in the Upper Kuskokwim region, Alaska, U.S.A. As a traditional food, salmon has been an important part of culture for the Upper Kuskokwim Athabascan people. Intensive contacts with non-Natives in the early 20th century brought some changes to Upper Kuskokwim people’s subsistence technologies including fishwheels, which made it possible to obtain large amount of salmon efficiently in siltladen main streams of the Upper Kuskokwim tributaries. Conflicts with non-Native wildlife management regime began after Alaska’s statehood when the State banned salmon fishing technology which involves blocking the entire width of a river or stream. As a result, Upper Kuskokwim people were forced to abandon their fishing weirs and fences at Salmon River since the late 1960s. After a decade or so, subsistence salmon fishing with rods and reels resumed at Salmon River. Nowadays, Salmon River Culture Camp has been organized by Nikolai Village Council to revitalize their fishing traditions. Since the 2010s, severe decline of king salmon populations in Alaska and Yukon has become a serious issue in indigenous societies of the areas. Local people think that commercial fishing (including bycatch) in high sea negatively affects the king salmon populations, while some others point out that increased activities by beavers and low-level of water in interior rivers might have been causing disruption of salmon's upstream migration. Through my observation of people’s activities in salmon spawning areas, I argue that making a small opening to beaver dams (instead of totally destroying them) may actually benefit spawning salmon populations.

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), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.613
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0000.002
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.009
GPT teacher head0.167
Teacher spread0.159 · 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 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

Citations0
Published2019
Admission routes1
Has abstractyes

Explore more

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