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Record W7099357916

(www.interscience.wiley.com). DOI: 10.1002/aqc.1063 Impact of recreational power boating on two populations of northern map turtles (Graptemys geographica)

2016· article· en· W7099357916 on OpenAlexaboutno aff

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicAdvanced Image and Video Retrieval Techniques
Canadian institutionsnot available
Fundersnot available
KeywordsHabitatPopulationWildlifeRecreationJuvenileWildlife conservationPopulation declineWildlife management
DOInot available

Abstract

fetched live from OpenAlex

1. Recreational power boating is growing in popularity in North America. This activity is known to have lethal and sub-lethal effects on aquatic wildlife and freshwater turtles may be particularly sensitive to this activity. 2. This study reports on patterns of traumatic injuries inflicted by powerboat propellers to northern map turtles (Graptemys geographica) from two sites differing in boat traffic intensity in Ontario, Canada. 3. The relative vulnerability of turtles was assessed, in light of seasonal patterns in boat traffic, as a function of sex- and age-specific movement patterns, habitat use, and basking behaviour obtained by radio-telemetry. Population viability analyses (PVA) were also conducted to evaluate the potential demographic consequences of mortality induced by powerboats. 4. The prevalence of propeller injuries was two to nine times higher in adult females than in adult males and juvenile females. Patterns of movement, habitat use, and aquatic basking indicated that adult females are more exposed to collisions with boats. PVA showed that boat-induced mortality in adult females could lead to rapid population extinction if the risk of mortality when hit by a boat is greater than 10%. 5. The results of this study showed that recreational power boating is a serious threat to northern map turtles, even under moderate boat traffic. The need to adopt measures restricting boat traffic in areas important to turtles is discussed.

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)0.000
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.843
Threshold uncertainty score0.690

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.026
GPT teacher head0.327
Teacher spread0.301 · 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

Citations0
Published2016
Admission routes1
Has abstractyes

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