Weaving Indigenous knowledge and western science to investigate the impacts of railways on wildlife
Bibliographic record
Abstract
Railways have been documented to cause mortalities for many different species, but overall, the ecological impacts of railways are under-researched and poorly understood. To date, railway ecology research has mainly focused on large mammals, but to develop effective railway mitigation, it is important to understand risks for underrepresented taxa. My aim was to use a Two-Eyed Seeing approach that weaved Indigenous knowledge and western science to improve understanding of railway ecology for understudied species and to help guide future mitigation efforts. In partnership with two First Nations, community members were invited to share Indigenous knowledge (IK) of wildlife-railway interactions to inform study design, then I conducted weekly visual surveys over three field seasons along two 3.6 km sections of railway in Eastern Georgian Bay, Ontario, recording the locations of live and dead wildlife. I recorded 462 observations of individuals from 42 different species, of which 76% were found dead, and 24% were encountered alive, findings complemented by shared IK. Reptiles and amphibians were the most severely impacted taxa, accounting for 87% of observed mortalities. Additionally, I identified hotspots of turtle and anuran interactions, and found that the locations of interactions were related to adjacent habitat use and railway features. Ultimately, this study highlights the value of collaborative research that uses complementary knowledge systems, indicates that reptiles and amphibians may be particularly susceptible to railway mortality, and identifies areas to target future mitigation both locally and in relation to broad scale landscape features for turtles and anurans.
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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 | 0.008 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.005 | 0.008 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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.
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".