Corroboration of biogeoclimatic ecosystem classification climate zonation by spatially modelled climate data
Bibliographic record
Abstract
The biogeoclimatic ecosystem classification (BEC) method for distinguishing areas of reasonably homogeneous macroclimate has been used in British Columbia for over 20 years. Because of the paucity of actual long-term climate data, the method used other means to map climate. We tested how well the BEC climate units could be discriminated from one another using spatially modelled climate data. We tested the ability of climate data to distinguish three units for each of four climatically different zones at two levels of the climatic classification using discriminant analysis. For each analysis, 60 points were randomly selected from within the boundaries of the mapped unit and climate data were generated by ClimateBC. Even at the finest level of the mapping, over 70% of the randomly selected points were correctly classified according to the mapped unit based on selected climate variables. A large proportion of the misclassified points were within 1 km horizontal distance or 100 m elevation of the boundary and are typically climatically transitional areas. We recommend that the BEC climate unit should form the basic unit for examining climate change at multiple scales from the provincial scale to the scale of watersheds or basins, and that further analysis be conducted to both improve biogeoclimatic unit mapping and climate models.
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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 | 0.011 | 0.040 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".