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Record W6986991077

A rocky solution: evaluating the use of common construction materials as road-effect mitigation for turtle communities in a rock barren landscape

2023· dissertation· en· W6986991077 on OpenAlexaboutno aff

Bibliographic record

VenueLu Zone Ul (Laurentian University) · 2023
Typedissertation
Languageen
FieldEnvironmental Science
TopicWildlife-Road Interactions and Conservation
Canadian institutionsnot available
Fundersnot available
KeywordsHabitatTurtle (robot)Nesting (process)PopulationNest (protein structural motif)SnagShouldersWildlifeBiological dispersal
DOInot available

Abstract

fetched live from OpenAlex

Roads are pervasive linear features that bisect landscapes, altering how female turtles use and
\nmove between critical habitats during nesting migrations. While turtle population viability
\ndepends on the survivorship of reproductive females, few cost-effective mitigation strategies
\ndirectly address their vulnerability to roads. The objective of my study was to evaluate a new
\ncost-effective mitigation strategy that used common construction materials to reduce road threats
\nfor turtle communities in eastern Georgian Bay, Ontario. The mitigation design aimed to deter
\nfemales from nesting in roadside habitats by replacing ~ 300 m of exposed gravel at 5 wetland
\ncrossings with rock rip-rap and paved road shoulders (using tar-and-chip), while the rest of the
\nroad shoulders remained unchanged. The success of the mitigation strategy was assessed by
\nwhether it successfully prevented females from using the road as nesting habitat. First, I used a
\nBefore-During-After comparison of nesting observations and nest hot spots on the road. I found
\na 15% decrease in the number of females nesting at Mitigated sites in the After period; however,
\nfemales continued to nest in the nearest available Unmitigated roadside habitat, including in
\nsemi-compact tar-and-chip road shoulders at Mitigated sites. In addition, nesting hot spots
\nremained at Mitigated sites in the After period. Second, I investigated the availability and
\nsuitability of natural nesting habitats in the surrounding rock barren landscape relative to nesting
\nhabitats used by female turtles on road shoulders. I conducted systematic habitat surveys (in
\n10800 1m x 1m plots) to quantify the availability of suitable nesting habitats on open rock
\nbarrens based on soil depth and canopy openness requirements for Species at Risk (SAR) turtles.
\nI found road shoulders met nesting habitat requirements for three local turtle species, whereas
\nonly 1% of rock barrens in the 231-ha study area were suitable for turtle nesting. Overall, I found
\nthe availability of suitable nesting habitats was limited across the natural landscape, which may
\ncontribute to females’ selection of roadside nesting habitats. My findings suggest that the
\nmitigation strategy was unsuccessful at deterring female turtles from nesting on roads and should
\nnot be applied without further research, especially in areas where natural nesting habitat may be
\nlimited. I identified additional recovery actions, such as mortality mitigation (i.e., fenceunderpass mitigation) and nesting habitat restoration, that may be required to reduce road effects
\nfor the turtle community. Overall, my project contributes to studies evaluating road-effect
\nmitigation and highlights several important findings that can be incorporated into Best
\nManagement Practices for turtles during road development.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.256
Threshold uncertainty score1.000

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 communication0.0000.000
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 head0.026
GPT teacher head0.257
Teacher spread0.231 · 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.

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
Published2023
Admission routes1
Has abstractyes

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