MétaCan
Menu
Back to cohort
Record W7133289797

Advice on a monitoring program for redside dace

2023· other· en· W7133289797 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 · 2023
Typeother
Languageen
Field
Topic
Canadian institutionsnot available
Fundersnot available
KeywordsSampling (signal processing)Sampling designScale (ratio)ImperfectSample (material)Range (aeronautics)Adaptive managementEvent (particle physics)
DOInot available

Abstract

fetched live from OpenAlex

Redside Dace, a species listed as Endangered under the Species at Risk Act, has experienced severe declines throughout its Canadian range over the past 30 years. Federal and provincial recovery strategies indicate that the development of a long-term monitoring program to inform recovery and management decisions is a high priority recovery action. Distribution- and abundance-based indicators should be chosen to allow Redside Dace to be assessed relative to management objectives. Failure to clearly specify monitoring program objectives can lead to poor study design and an inability to understand the conservation status of the species or the influence of threats and recovery measures. The ability to detect changes through time is contingent on the application of a standardized monitoring approach. Monitoring design can include several spatial scales (site-level, sub-watershed, population, Canadian range). The choice of sampling scale is dependent on management objectives. Measuring the distribution and abundance of Redside Dace can be biased by imperfect detection, which is the failure to detect the species despite its occurrence. Field sampling design based on repeated surveys, and related modelling approaches, exist to account for imperfect detection. Addressing imperfect detection will improve upon previous guidance for monitoring Redside Dace. The ability to detect changes in distribution (occupancy) or trends through time is contingent on sampling efficiency, the occurrence of the species, the number of sampling sites, and the frequency of sampling. Many sites are required to detect small changes in occupancy; whereas, fewer sites are needed to detect large changes. Improved sampling efficiency will reduce effort requirements. Greater confidence in monitoring results will require increased sampling effort. Several gears exist to detect Redside Dace. Improvements to sampling design advice will require further evaluation of detection probability and harm imposed by each gear.

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)0.004
metaresearch head score (Gemma)0.011
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: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.943
Threshold uncertainty score0.265

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.011
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0030.001
Science and technology studies0.0010.001
Scholarly communication0.0010.002
Open science0.0030.001
Research integrity0.0030.002
Insufficient payload (model declined to judge)0.0790.019

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 head0.015
GPT teacher head0.278
Teacher spread0.263 · 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
Published2023
Admission routes1
Has abstractyes

Explore more

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