Data From: Urban planning for wildlife connectivity, a multispecies assessment of urban sprawl and SLOSS renaturalization strategies
Bibliographic record
Abstract
Data use for the analytical process in manuscript "Urban planning for wildlife connectivity, a multispecies assessment of urban sprawl and SLOSS renaturalization strategies" Journal of Applied Ecology, 2025 File: roadkill_Toronto_GElmi_Cadusso_JAPP_manuscript.zip Description: Roadkill data used for the validation of the Toronto connectivity map. Data includes both city of Toronto road kill data, through the Toronto Wildlife Centre and queried and sorted inaturalist datapoints whereby keywords dead or roadkill where found, for all mammals, amphibians and reptiles. File: input.zip Description: Compressed folder contains all input maps for all taxa and all scenario sorted in folder hieracy taxa > scenario > maps. Resistance maps are distinguished between those destined for graphab maps, identified as _graphab, and those destined for omniscape, Access information Other publicly accessible locations of the data: None Data was derived from the following sources: inaturalist, city of Toronto, Toronto Wildlife Centre, Toronto Region Conservation Authority For Methods refer to source manuscript.
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 imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.075 | 0.037 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".