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Record W7019802122

Impacts of a windfarm and wildfire on the spatial ecology and habitat selection of an endangered freshwater turtle

2022· dissertation· en· W7019802122 on OpenAlexaboutno aff

Bibliographic record

VenueLu Zone Ul (Laurentian University) · 2022
Typedissertation
Languageen
FieldSocial Sciences
TopicMigration, Ethnicity, and Economy
Canadian institutionsnot available
Fundersnot available
KeywordsHabitatTurtle (robot)Endangered speciesHome rangeRange (aeronautics)Spatial ecologyForagingSelection (genetic algorithm)
DOInot available

Abstract

fetched live from OpenAlex

Wind is a source of renewable energy, and its use is projected to increase as governments look for solutions to reduce carbon emissions. Although wind energy presents many advantages, windfarms can pose risks to wildlife. I used a post-hoc design to investigate whether body condition, spatial ecology, and multiscale habitat selection by spotted turtles (Clemmys guttata) differed among three treatment sites in central Ontario: Control, windfarm (Wind), and combined post-wildfire and windfarm (Windburn). I outfitted 9-10 turtles per treatment with VHF radio transmitters and tracked them approximately twice per week throughout the active season. Body condition, home range size and minimum daily distances moved by turtles did not differ among treatments, but it is possible that I did not detect acute responses to the habitat modification because turtles may have had sufficient time to adapt their behaviours as my study was conducted 2 years post-construction and 2.5 years post-wildfire. Turtles did not avoid habitats near turbines or roads but also did not cross roads unless a semi-aquatic culvert was present, highlighting the need to maintain habitat connectivity. In Windburn, turtles used wet depressions on rock barrens while Control and Wind turtles did not, possibly because Windburn turtles were exploiting new early successional macrohabitat resulting from the wildfire; however, pre-wildfire movement data would be required to confirm cause. In all treatments, turtles selected microhabitat based on temperature, water depth, available cover, and hummock presence, suggesting that turtles were able to find suitable microsites in the modified landscapes of my study area. My study is one of the first to assess the impacts of windfarms on semi-aquatic turtles, an at-risk and understudied taxon on windfarms, but more research is required to understand the acute and long-term impacts of windfarms and wildfires on turtles to inform data-driven mitigation strategies.

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)0.000
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 score0.083
Threshold uncertainty score0.164

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
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 head0.226
Teacher spread0.217 · 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
Published2022
Admission routes1
Has abstractyes

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