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Record W3195089972 · doi:10.32920/ryerson.14648697.v1

How to get the salamander across the road: exploring the policy intersection of biodiversity conservation and road projects in Ontario

2021· preprint· en· W3195089972 on OpenAlexafffundabout
Joshua T. Wise

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEnvironmental Science
TopicWildlife-Road Interactions and Conservation
Canadian institutionsUniversity of GuelphToronto Metropolitan UniversityWestern University
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsWildlifeBiodiversityWildlife corridorGeographyHabitatEnvironmental resource managementWildlife conservationEnvironmental planningEcologyEnvironmental science

Abstract

fetched live from OpenAlex

Connecting landscapes around roads is an important element in a broader strategy to help protect and recover biodiversity. In regions like southern Ontario and the Greater Golden Horseshoe, growing urban footprints are leading to an expansion of road networks. Road planning and design has historically fragmented natural habitat and created barriers for wildlife movement. The negative impacts of roads can be mitigated through the creation of wildlife crossing structures that enable safe passage of wildlife over or under roads. This Major Research Paper will investigate key Ontario land use and regulatory policies that intersect with both road projects and biodiversity recovery to evaluate their effectiveness in recognizing biodiversity values and enabling the creation of wildlife crossing structures. Key words: landscape connectivity, wildlife crossing, safe passage, biodiversity, conservation, policy, planning, Ontario, Greater Golden Horseshoe

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)0.001
metaresearch head score (Gemma)0.003
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 score0.089
Threshold uncertainty score0.648

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0080.003
Scholarly communication0.0030.001
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.000

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 head0.057
GPT teacher head0.256
Teacher spread0.198 · 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
Published2021
Admission routes3
Has abstractyes

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