Integrating socio‐economic and biophysical models: revision of a land‐use allocation model
Bibliographic record
Abstract
Abstract Integration of large‐area, economically driven macro‐models and small‐area, biophysically based models in the Canadian agricultural sector was described recently in the development of a land‐use allocation model ( LUAM ). We have since developed and integrated an improved methodology for allocating crop area changes generated for large areas by the Canadian Regional Agricultural Model ( CRAM ) to much smaller Soil Landscapes of Canada ( SLC ) polygons. Validation of outputs showed considerable improvement. The new coefficients of determination ( R 2 ) between simulated and actual data, with previous values in brackets, were 0.69 for fodder corn (0.54), 0.88 for wheat (0.62), 0.77 for hay (0.26), 0.54 for alfalfa (not previously reported), 0.88 for soya bean (0.26) and 0.86 for grain corn (0.22). The best result was obtained for soya bean, with a normalized root mean square error ( NRMSE ) of 0.31%, and the poorest for alfalfa, with NRMSE = 17.34%.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".