Tragedy of the culverts? Characterizing the state of road infrastructure in public and private forests
Bibliographic record
Abstract
Forests are often crisscrossed by a vast road network due to extractive activity. Previous studies have shown that this network can include many abandoned logging roads and deteriorated culverts, which can disrupt aquatic habitat connectivity. Yet, there is still little known about the drivers of culvert condition. In the absence of accurate and up-to-date surveys of forest road infrastructure, understanding drivers of culvert condition is integral in informing restoration efforts. Our study evaluated and compared the condition of 121 culverts and the roads directly adjacent to them in public and private forests in southern Quebec, Canada. The majority (63%) of the culverts inventoried were in poor condition ('Mediocre' or 'Critical'). Overall, 63% of culverts assessed along forest roads were in poor condition. The defects most frequently observed in poor condition culverts were: obstruction, perforation, and corrosion. When taking both culvert condition and culvert hang into consideration, this translated to one culvert in every 10.1 km of stream potentially posing a threat to aquatic habitat connectivity. Within the study region, land tenure (private vs public land) and road condition were both drivers of poor culvert condition. Culverts should be considered an action priority, given the magnitude of their impact on aquatic ecosystems and the dearth of information regarding their location and overall condition. The results of our study highlight the necessity for standardized protocols and continued forestry infrastructure inventories. Improved knowledge of drivers of culvert condition and of their primary defects is also central to designing effective maintenance and restoration efforts.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".