Predictive Ecosystem Mapping of the Chinchaga Watershed Management Basin, North Eastern British Columbia
Bibliographic record
Abstract
The purpose of this project was to identify and map the location of wetlands in the Chinchaga Watershed Management Basin within the Fort St. John Timber Supply Area. The Chinchaga study area covers 75,000 hectares (ha), and serves as an ecologically significant unit within the Fort St. John Timber Supply Area, representing the gentle, low relief terrain characteristic to Boreal Plains. The products created in this project are intended to provide input for the amendment of the Fort St. John Land & Resources Management Plan. Input data for this project consisted of Sentinel 2 spectral imagery, the provincial Digital Elevation Model, ClimateBC data, and Predictive Ecosystem Mapping provincial products. Both spectral and topographic indices were created to assist in model performance. A geographically stratified sampling approach was taken in order to generate 1012 points. These training points were attributed to allow for the prediction of three simplified land cover realms, as well as seven identified provincial Eco-Groups across the Chinchaga. Together, the explanatory variables and training points were fed into a Random Forest model, and the classes were predicted across the study area with 84.9% (Three Class) and 78.2% (Seven Class) overall accuracy, respectively. Included in this report are a brief discussion on explanatory variable contribution to model performance, as well as an analysis of sample design and its implications on class accuracies in the 7 Class prediction.
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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 | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".