Improving the spatial resolution and ecostratification of crop yield estimates in Canada
Bibliographic record
Abstract
Du, Y., Huffman, T., Daneshfar, B., Green, M., Feng, F., Liu, J., Liu, T. and Liu, H. 2015. Improving the spatial resolution and ecostratification of crop yield estimates in Canada. Can. J. Soil Sci. 95: 287–297. Canada's terrestrial ecostratification framework provides nested spatial units for organizing national data related to soils, landforms and land use. In the agricultural domain, the lack of national, uniform crop yield data on the ecostratification framework severely hinders our ability to evaluate the biophysical data with respect to economic and climatic conditions. We developed a national crop yield database at the regional (ecodistrict) level by aggregating individual records of an existing but very broad-level sample-derived yield database according to the ecostratification hierarchy. Issues related to the different sampling frameworks and the need for confidentiality of individual records were resolved in order to generate an ecostratified crop yield dataset at a reasonably detailed spatial scale. Sixty crops were first statistically arranged into 37 agronomically similar crop groups in order to increase class size, and these crop groups were aggregated into increasingly large spatial units until confidentiality was assured. The methodology maintained data quality and confidentiality while producing crop yield estimates at the ecodistrict level. Comparison to independent crop insurance data confirmed that the resulting crop yield data are valid where estimates were derived from data released at the level of an ecodistrict or an ecoregion, but not at the ecoprovince level. Our crop yield estimates offer a reasonably high level of spatial precision while remaining within standard confidentiality constraints.
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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 | 0.001 | 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".