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Record W2465814343 · doi:10.1017/s0018246x16000121

A SORRY TALE: NATIVES, SETTLERS, AND THE SALMON OF LAKE ONTARIO, 1780–1900

2016· article· en· W2465814343 on OpenAlexaboutno aff
Karim M. Tiro

Bibliographic record

VenueThe Historical Journal · 2016
Typearticle
Languageen
FieldEnvironmental Science
TopicEcology and biodiversity studies
Canadian institutionsnot available
Fundersnot available
KeywordsFishingFish <Actinopterygii>FisheryPopulationDeforestation (computer science)AgricultureGeographyFish farmingHabitatEcologyArchaeologyAquacultureBiologyDemographySociology

Abstract

fetched live from OpenAlex

Abstract Through the end of the eighteenth century, Lake Ontario had a large population of Atlantic salmon. However, the salmon population declined precipitously in the first half of the nineteenth century, and the fish had disappeared completely by 1900. This article analyses the responses of both Natives and settlers to initial salmon abundance and subsequent diminution. Although the extirpation of the lake's salmon is generally attributed to the construction of dams, this article identifies earlier and broader causes of salmon decline. In both Canada and the United States, commercial fishing captured unprecedented numbers of fish while agriculture and deforestation compromised salmon spawning habitat. While primary responsibility for the extirpation rests with the settlers, both Natives and Euro-Americans treated the fish as a commodity. As the salmon dwindled, sportsmen's groups came to the fore in setting fisheries policy. Sportsmen supported enhanced conservation measures but based their strategy on unrealistic methods for reviving the fish population through pisciculture.

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)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.051
Threshold uncertainty score0.103

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.002
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.010
GPT teacher head0.176
Teacher spread0.166 · 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 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

Citations13
Published2016
Admission routes1
Has abstractyes

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