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Record W4412981550 · doi:10.1139/cjfas-2025-0002

Evaluation of federal habitat stewardship projects for aquatic species at risk in Canada: the province of Ontario as a case study

2025· article· en· W4412981550 on OpenAlexaffvenueabout
Dominique Rumball, Nicholas E. Mandrak

Bibliographic record

VenueCanadian Journal of Fisheries and Aquatic Sciences · 2025
Typearticle
Languageen
FieldEnvironmental Science
TopicEnvironmental Conservation and Management
Canadian institutionsThe Scarborough Hospital
Fundersnot available
KeywordsStewardship (theology)HabitatEcologyEnvironmental protectionGeographyBiology

Abstract

fetched live from OpenAlex

The Habitat Stewardship Program (HSP) is a major pillar in Canada's National Strategy for the Protection of Species at Risk that supports projects assisting the recovery of species at risk (SAR). To assess long-term and biological outcomes and evaluate taxonomic and geographic coverage of HSP-funded projects using Ontario as a case study, 95 HSP-funded projects for aquatic SAR (2006–2017) were evaluated. Fifteen million CAD was allocated to 66 habitat stewardship projects targeting aquatic SAR. Twenty-nine fish and mussel SAR were targeted in 12 watersheds located primarily in southwestern Ontario. Greater funding was allocated to multi-year projects targeting higher numbers of SAR. Future funding should target geographically widespread SAR and SAR-rich watersheds in southwestern Ontario. To align stewardship actions with aquatic SAR recovery priorities, proposals and reports must identify the threats being addressed by stewardship actions. Standardized pre- and post-activity monitoring should be required to ensure measurable conservation benefits and to enable assessment of the effectiveness of habitat stewardship actions. Similar analyses should be undertaken in other Canadian provinces and territories to evaluate the HSP nationwide.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.064
Threshold uncertainty score0.884

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.039
GPT teacher head0.242
Teacher spread0.204 · 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.

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

Citations1
Published2025
Admission routes3
Has abstractyes

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