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Record W4256633480 · doi:10.47886/9781934874417.ch10

Managing the Impacts of Human Activities on Fish Habitat: The Governance, Practices, and Science

2015· book-chapter· en· W4256633480 on OpenAlexaboutno aff

Bibliographic record

VenueAmerican Fisheries Society eBooks · 2015
Typebook-chapter
Languageen
FieldEnvironmental Science
TopicFish Ecology and Management Studies
Canadian institutionsnot available
Fundersnot available
KeywordsFisherySpawn (biology)Cumulative effectsGeographyFish migrationFishingHydroelectricityHabitatFish <Actinopterygii>Environmental scienceEcologyBiology

Abstract

fetched live from OpenAlex

&lt;em&gt;Abstract.&lt;/em&gt;—American Eel mortality has increased substantially over the past century due largely to significant cumulative effects of fishing and fish passage through hydro-electric turbines across their range. Nowhere has this been more pronounced than in waters of the St. Lawrence River, Lake Ontario, Ottawa River and associated watersheds. We illustrate this by examining the cumulative effects of hydro-electric facilities on eels migrating downstream through the Mississippi River and Ottawa River, and outline further impacts eels encounter en route to spawn in the Sargasso Sea. The probability of a mature female eel surviving its emigration through the Mississippi and Ottawa River to the upper St. Lawrence River is estimated to be as low as 2.8% due to turbine mortalities alone (2.8–40%). Mortality risk increases as the eel attempts to run the gauntlet of fisheries in the lower St. Lawrence River and the probability of out-migration survival is estimated to be as low as 1.4%. Some mortalities could be mitigated through improved application of existing laws, development of policy requiring consideration of cumulative effects and improved integration among program areas responsible for sustainable management of fisheries, biodiversity, dams and hydro-electric facilities. We recommend changes to policy, procedures and internal organizational structures provided with clear directions, and call for increased accommodation of Aboriginal perspectives.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.530
Threshold uncertainty score0.985

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.018
Scholarly communication0.0000.000
Open science0.0010.001
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 head0.021
GPT teacher head0.247
Teacher spread0.226 · 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
Published2015
Admission routes1
Has abstractyes

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