A geospatial model for characterizing the fish resources of the Similkameen River, British Columbia, Canada
Bibliographic record
Abstract
A geospatial model was been developed in order to rapidly characterize fluvial geomorphological features associated with the fish resources in a river system. The model uses four easily-quantified geospatial attributes – channel width, plan view sinuosity, longitudinal slope and fractal dimension – for classifying a stream channel into geomorphic response units (GRUs), which are the key working elements of the geospatial model used in this work. Using the geospatial model, a total of five GRUs were defined along the river channel. The model framework was tested using data from a 1983 fish survey conducted along the Canadian portion of the Similkameen River. Five fish species were sampled in that survey: rainbow trout, mountain whitefish, sculpin, longnose dace and bridgelip sucker. A hierarchical clustering analysis was conducted using the fish survey data, with good correlation being observed between the fish data clusters and geospatial model GRUs. It is concluded that, on the basis of the work reported herein, the geospatial modelling approach provides a simple, rapid tool for a priori classification of the fish resources in a stream.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".