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Record W7133269135

Threats to fish habitat

2024· other· en· W7133269135 on OpenAlexfundno 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 · 2024
Typeother
Languageen
Field
Topic
Canadian institutionsnot available
FundersFisheries and Oceans Canada
KeywordsHabitatFish habitatFish <Actinopterygii>Range (aeronautics)Geospatial analysisClimate changeHabitat destruction
DOInot available

Abstract

fetched live from OpenAlex

The work presents an approach for compiling and quantifying a large amount of spatial information to estimate threats to fish and fish habitat in the Fraser River Basin (FRB), including nine anthropogenic threats, four climate-change related threats, and cumulative threat scores, using readily available data. For this document, threats are defined as the exposure of fish and fish habitat to anthropogenic activities and climate change. Additional information on the sensitivity of focal fish and fish habitats to the identified threats (such as stressor-response relationships) was beyond the scope of this analysis but would be needed to develop cumulative effects mapping. The approaches to estimating each of the indicators provide an initial broad-scale standardized framework that can be applied to characterize threats throughout the Pacific Region. Further, the approach presented incorporates many of the desirable features of geospatial mapping tools for fish and fish habitat identified in DFO (2022). Generally, Species At Risk (SAR) habitat with limited ranges (i.e., Coastrange Sculpin, Green Sturgeon, Nooksack Dace, and Salish Sucker) had higher median human activity cumulative threat scores relative to all streams in the FRB. Conversely, median human activity threat scores tended to be similar among Salmon Conservation Units (CUs) and relative to all streams, which is driven in part by the large extent of CUs that inherently capture a greater range of threat scores across streams. Re-assessing threats temporally was considered largely feasible based on updates to the included data, and by using the current threat assessment as a baseline. Example applications of the threat scores and associated inputs for informing management and prioritization decisions for Salmon habitat in the Thompson-Nicola Ecological Drainage Unit (EDU), particularly in the context of climate change were conducted: The Deadman and Adams River watershed groups were identified as having high cumulative composite scores under current and future climate conditions across Salmon species in the EDU. The riparian input composite score identified high scores including along the North Thompson River, Eagle River, and Shuswap River based on nonpoint source inputs, riparian disturbance, and modeled environmental favourability (probability of occurrence) for Salmon spawning. The water resource composite score found the South Thompson River watershed had high scores across Salmon species based on co-occurrence of high water withdrawal allowances and low stream flows. The anadromous fragmentation score identified high variation in this metric across the EDU, based on modeled environmental favourability above dams that are full barriers. Considerations for application: The analytical approach would be strengthened by sensitivity analyses and validation with independent data. Currently, confidence in the relative characterization of threat scores (including cumulative threat scores) is uncertain. Recommendations for future analyses include developing and applying metrics for levels of confidence in threat scores, which could be based on expert review or formal criteria. A variety of improvements and alternatives to individual and cumulative threat scores are provided for consideration. It is recommended that uncertainty in outputs be considered prior to applying the approach to inform fish and fish habitat management decisions. This broad-scale tool can provide insight into within-watershed planning and prioritization. Local-scale application may be further informed by local expertise, Indigenous knowledge, salmon population data, and finer-scale tools.

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)0.002
metaresearch head score (Gemma)0.005
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 score0.959
Threshold uncertainty score0.081

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0100.006
Science and technology studies0.0010.001
Scholarly communication0.0020.002
Open science0.0010.003
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0060.001

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 head0.012
GPT teacher head0.253
Teacher spread0.242 · 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
Published2024
Admission routes1
Has abstractyes

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