Remote sensing framework details riverscape connectivity fragmentation and fish passability in a forested landscape
Bibliographic record
Abstract
Fragmentation of stream networks from anthropogenic structures such as road culverts can affect the health of a catchment by negatively affecting the ecosystem’s biota, their movements, abundance, and species richness. We present a framework using publicly available LiDAR and orthophotography to locate and identify road crossings, i.e. the most prolific of barriers in forested landscapes, and evaluate fragmentation and passability at the landscape scale. Coupling the LiDAR stream network and private road network in the 3,223 km2 study area, we identified 1,052 stream crossings of which, 32% were culverts and 12% of the total stream network was potentially inaccessible due to these culverts. We correctly identified the type of stream-road crossings at >90% of any stream order and at 100% at Orders >2. The 10 culverts restricting the most stream kilometers, restricted >34% of the potential stream habitats for four species of fish, a result that provides the resource management with a first assessment for effective improvement of connectivity across this landscape. With this framework, managers equipped with appropriate imagery can create a stream crossing database with minimal funding, create an inventory of instream barriers, and prioritize removals at a landscape-scale, thus providing an effective assessment and decision-making tool for their habitat 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".