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Record W4232668509 · doi:10.47886/9781934874417.ch2

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

2015· book-chapter· en· W4232668509 on OpenAlexaboutno aff

Bibliographic record

VenueAmerican Fisheries Society eBooks · 2015
Typebook-chapter
Languageen
FieldSocial Sciences
TopicArctic and Russian Policy Studies
Canadian institutionsnot available
Fundersnot available
KeywordsFisheries managementCorporate governanceFisheryBusinessPopulationStatuteHabitatEnvironmental planningFish stockFish <Actinopterygii>Environmental resource managementFishingGeographyPolitical scienceEcologyBiologyLawEconomicsFinance

Abstract

fetched live from OpenAlex

Abstract.—Canada’s Fisheries Act, the country’s primary law for regulating the harvesting of its marine and freshwater fisheries resources, includes provisions to regulate the impacts of human activities on fish and fish habitat. As a result of these provisions the Fisheries Act represents the main federal statute for protecting freshwater and marine aquatic ecosystems and is considered one of the strongest environmental laws in Canada. This paper outlines the legal and policy frameworks and institutional arrangements for the administration of these provisions of the Fisheries Act. It describes the review process and practices established by Fisheries and Oceans Canada (DFO) for administering the provisions of the Act assigned to the Department’s Fish Habitat Management Program (HMP). It defines the key issues and concerns raised about the delivery of the Fish Habitat Management Program and reviews initiatives undertaken to address these. It suggests that while these have improved delivery of the regulatory responsibilities of the HMP, there is a need for more fundamental changes that will enable it to keep pace with the increasing and cumulative impacts associated with population growth and economic development and create conditions under which human activities and fish and fish habitat can co-exist on a sustainable basis. This paper suggests that such a change must be founded on an ecosystem-based approach and on the application of modern scientific and management principles for regulating impacts to fish and fish habitat. It also describes steps to move forward to demonstrate and instill an ecosystem-based approach.

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)0.006
metaresearch head score (Gemma)0.008
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.867
Threshold uncertainty score0.305

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.003
Science and technology studies0.0050.018
Scholarly communication0.0110.002
Open science0.0010.003
Research integrity0.0010.001
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 head0.044
GPT teacher head0.316
Teacher spread0.273 · 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 designNot applicable
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
Published2015
Admission routes1
Has abstractyes

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