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Record W7133271083

Science advice on the use of timing windows as a mitigation measure

2022· other· en· W7133271083 on OpenAlexaboutno aff
Fisheries and Oceans Canada, Pêches et Océans Canada

Bibliographic record

VenueFederal Open Science Repository of Canada / Le Dépôt fédéral de science ouverte du Canada · 2022
Typeother
Languageen
Field
Topic
Canadian institutionsnot available
Fundersnot available
KeywordsVulnerability (computing)HabitatFish <Actinopterygii>Process (computing)Conceptual modelVariation (astronomy)Fish habitat
DOInot available

Abstract

fetched live from OpenAlex

Timing windows are a mitigation measure that define periods in the year when a work, undertaking, or activity (WUA) can take place because the potential effects of that WUA on fish and fish habitat are reduced relative to other times of the year. Timing windows are an appropriate mitigation measure when the pressures caused by the WUA are transient, and when there is predictable variation in the vulnerability of fish and fish habitat to WUA pressures over time. Considerable variation was observed among established timing windows in Canada, reflecting the diversity of species and habitats across the country. However, there is limited science on the development, use, and effectiveness of timing windows. Thus, there is a need for a scientific process for their development and modification to facilitate their standardization and defensibility, and to validate their effectiveness. A conceptual model for the development and refinement of timing windows was presented that can be used to identify periods of the year when risks to fish and fish habitat from WUA pressures are lower. The conceptual model includes (1) the timing of life processes of species of interest, (2) the relative vulnerability of each life process to WUA pressures, (3) seasonal variation in environmental conditions, and (4) an assessment of how magnitude and persistence of the effects of WUAs vary due to modulation by environmental conditions. The four components of the conceptual model can be used to inform an assessment of the variation in the risk to fish and fish habitat from WUA pressures throughout the year. Although the model was presented for a single fish species or groups of species with similar life histories, conceptually, it can be adapted for communities or guilds of species. Based on this conceptual model, timing windows can be established for periods when the risk to fish and fish habitat is assessed to be reduced. Uncertainty (due to lack of knowledge, or to spatial and temporal variation in biological and ecological processes) can be managed by varying the duration of timing windows in accordance with risk tolerances and management goals. Timing windows can be modified by including site-specific information on species biology and environmental conditions, and could also be modified as required in response to observed variation in biological or environmental events. Timing windows are one of a suite of mitigation measures commonly prescribed by FFHPP to reduce the risk of harmful impacts on fish and fish habitat. If a period of reduced risk cannot be identified and the impact of a WUA pressure cannot be mitigated by using this measure, timing windows may not be effective, and greater emphasis should be placed on the use of other measures. A three-tiered approach for evaluating the effectiveness of timing windows was adapted from previous advice. The first tier consists of monitoring to determine the extent of exposure of fish and fish habitat to WUA pressures during the timing window. The second tier is designed to establish if timing windows reduce fish mortality and/or impairment of the habitat’s capacity to support life processes of fish. The third tier is an assessment of potential higher order consequences (e.g., above individual or site level) of the WUA pressure during the timing windows, and has a goal of increasing our understanding of their use. Challenges associated with the implementation of the proposed approach include estimating the risk to fish and fish habitat for complex and diverse fish communities in spatially and temporally variable environments, incorporating the effects of climate change, and difficulties evaluating the effectiveness of timing windows

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)0.035
metaresearch head score (Gemma)0.093
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: Other · Consensus signal: none
Teacher disagreement score0.965
Threshold uncertainty score0.186

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0350.093
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0010.003
Bibliometrics0.0040.003
Science and technology studies0.0030.007
Scholarly communication0.0060.014
Open science0.0060.005
Research integrity0.0080.014
Insufficient payload (model declined to judge)0.0210.008

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 head0.022
GPT teacher head0.242
Teacher spread0.220 · 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
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
Published2022
Admission routes1
Has abstractyes

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Same venueFederal Open Science Repository of Canada / Le Dépôt fédéral de science ouverte du CanadaFrench-language works237,207