Development and evaluation of a new Canadian spring wheat sub-model for DNDC
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Kröbel, R., Smith, W. N., Grant, B. B., Desjardins, R. L., Campbell, C. A., Tremblay, N., Li, C. S., Zentner, R. P. and McConkey, B. G. 2011. Development and evaluation of a new Canadian spring wheat sub-model for DNDC. Can. J. Soil Sci. 91: 503-520. In this paper, the ability of the DNDC model (version 93) to predict biomass production, grain yield and plant nitrogen content was assessed using data from experiments at Swift Current, Saskatchewan, and St-Blaise, Quebec, Canada. While predicting wheat grain yields reasonably well, the model overestimated the growth of above-ground plant biomass and nitrogen uptake during the first half of the growing season. A new spring wheat sub-model (DNDC-CSW) was introduced with a modified plant biomass growth curve, dynamic plant C/N ratios and modified plant biomass fractioning curves. DNDC-CSW performed considerably better in simulating plant biomass [modeling efficiency (EF): 0.75, average relative error (ARE): 6.0%] and plant nitrogen content (EF: 0.61, ARE: -2.7%) at Swift Current and St-Blaise (EF of 0.75 and ARE of 2.3%), compared with DNDC 93 (biomass SC: EF 0.49, ARE 17.1%, SB: EF 0.02 ARE 33.4%). In comparison with DNDC 93, DNDC-CSW better captured inter-annual variations in crop growth for a range of wheat rotations, increasing the EF from 0.32 to 0.52 for grain and from 0.35 to 0.39 for straw yields. DNDC-CSW also performed considerably better than DNDC 93 in estimating soil carbon changes at Swift Current. Hence, DNDC-CSW has the potential to improve the performance of DNDC 93 in simulating wheat biomass, plant nitrogen, yield and soil carbon at various Canadian sites.
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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 it