Bibliographic record
Abstract
Changing climatic conditions on seasonal and longer time scales influence agricultural production. Improvement of soil and fertilizer is a strong factor in agricultural production, but agricultural production is influenced by climate conditions even in highly developed countries. It is valuable if fewer predictors make it possible to conduct future projections. Monthly temperature and precipitation, wintertime 500hPa geopotential height, and the previous year's yield are used as predictors to forecast spring wheat yield in advance. Canadian small agricultural divisions (SAD) are used for analysis. Each SAD is composed of a collection of Canadian Agricultural Regions (CAR) of similar weather and growing conditions. Spring wheat yields in each CAR are forecast from the following variables: (a) the previous year's yield, (b) earlier stages of the growing season's climate conditions and, (c) the previous year's wintertime northern hemisphere 500hPa geopotential height field. Arctic outflow events in the Okanagan Valley in Canada are associated with episodes of extremely low temperatures during wintertime. Principal component analysis (PCA) is applied for wintertime northern hemisphere 500hPa geopotential height anomalies. The spatial PCA mode1 is defined as Arctic Oscillation and it influences prevailing westerlies. The prevailing westerlies meanders and influences climatic conditions. The spatial similarity between wintertime top 5 Arctic outflow event year's composites of 500hPa geopotential height anomalies and mode 3's spatial pattern is found. Mode 3's spatial pattern looks like the Pacific/North American (PNA) pattern which describes the variation of atmospheric circulation pattern over the Pacific Ocean and North America. Climate conditions from April to June, May to July, mode 3's time coefficients, and previous year's yield are used for forecasting spring wheat yield in each SAD. Cross-validation procedure which generates eight sets of models for the eight validation periods is used. To show the reproducing projection between observed and calculated values, the root mean squared error for skill score (RMSE SS) with the persistence model serving as the reference model is used. The persistence model is used as a benchmark. The results show that SADs near USA border show better RMSE SS values and mode 3's time coefficients can be a useful predictor especially for inland province such as Manitoba. Among 27 Canadian Prairie's SADs with perfect yield data, 67% of Alberta's SADs, 86% of Manitoba's SADs, and 77% of Saskatchewan's SADs can get positive skill scores. In each SAD, future yield projection is calculated applying predictors in 2013 for the obtained eight sets of models and eight sets of forecasted values in 2013 are averaged and a near future projection result is obtained. Series of outputs including calculated forecasted yield value in each SAD is provided by smart phone application. A system for providing climatic condition for a point with a permission of Climatic Research Unit - University of East Anglia and for obtaining patent is proposed. There are several patented systems similar to the system proposed in this paper. However, these patents are different in essence. The system proposed in this paper consists of two parts. First part is to estimate equations using time series data. The second part is to acquire and apply latest climatic conditions for obtained equations and calculate future projection. If the procedure is refined and devices are originally developed, series of idea can be patented. For future work, crop index, Hokkaido is also introduced.
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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".