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Record W3001141849

Restoring hydro-impacted wetlands for secretive marsh birds

2019· article· en· W3001141849 on OpenAlexaboutno aff
Ashleigh M. Westphal

Bibliographic record

VenueSummit (Simon Fraser University) · 2019
Typearticle
Languageen
FieldEnvironmental Science
TopicFish Ecology and Management Studies
Canadian institutionsnot available
Fundersnot available
KeywordsWetlandMarshEcologyGeographyFisheryBiology
DOInot available

Abstract

fetched live from OpenAlex

Secretive marsh birds can be difficult to detect and are dependent on wetlands, leaving them vulnerable to wetland loss or alteration. This study examines the influence of management-altered hydrological regimes on five secretive marsh bird species in the West Kootenay and Columbia Wetlands in British Columbia, Canada. Focal species occupied wetlands with less frequently altered hydrological regimes more often and in greater numbers. Occupancy models suggested that woody vegetation, tall vegetation, and open water are important drivers of occupancy for these species. Wetlands most frequently experiencing heavily altered hydrological regimes had more open water and less tall vegetation, both of which were negatively associated with wetland occupancy. Water management operations may be promoting altered vegetation communities within these wetlands, in turn promoting lower occupancy of secretive marsh bird species. Restoration recommendations include: prioritizing lower elevation wetlands, limiting woody vegetation encroachment, and experimentally restoring the hydrological regime of affected wetlands.

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)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.330
Threshold uncertainty score0.656

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.009
GPT teacher head0.197
Teacher spread0.187 · 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 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

Citations0
Published2019
Admission routes1
Has abstractyes

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