Using desktop hydrologic data to predict fish presence instreams in northern British Columbia
Bibliographic record
Abstract
Identification of fish-bearing streams is a key part of many environmental assessments in Canada in general, and specifically in British Columbia (BC), where fish and fish habitat are highly valued components of the natural environment. Pre-field identification of likely fish-bearing and non-fish-bearing streams has the potential to reduce cost and effort related to field inventories, and to expedite the project design process. Previous research has considered desktop level hydrologic, geologic and land-use data from single catchments with good results, but in some cases did not maintain simi- lar predictive success for distant catchments. This research drew from three distinct catchments, with the aim of developing a model that will be more generally applicable. Data on fish presence/absence, watershed area, and mean and maximum monthly flows was collected from 2055 stream crossing points as part of the environmental assessment for the Prince Rupert Gas Transmission (PRGT) project. Canadian Digital Elevation Data was used to identify the elevation and derive the slope for each site. Parameters derived from this data were assessed using logistic regression to develop a model for predicting fish-bearing status. The final model included the following parameters: watershed area, field gradient (as a proxy for higher-quality desktop slope values), number of months per year with maxi- mum flow ≥ the 80th percentile of maximum monthly flows, and latitude. The model achieved good predictive success for non-fish-bearing streams (79% to 91% correctly identified) but performed less well for fish-bearing streams (65% to 66% correctly iden- tified). The contrast between levels of predictive success was thought to be strongly influenced by the quality of the underlying data, where, for regulatory reasons, the actual status of streams classified as non-fish-bearing was likely far more certain than the status of streams classified as fish-bearing.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".