MAIS, a mechanistic model of maize growth and development
Bibliographic record
Abstract
Over the past 30 years the behavior of crops, i.e., systems consisting of a community of plants, has been analyzed using computer simulation techniques. Models of crop growth and development consist of an array of mathematical equations that are, almost without exception, based on a combination of empirical relationships and physical/biochemical principles. The objective of this study was to evaluate the strengths and weaknesses of the model MAIS, a model of maize growth and development. A detailed description of the code is presented with emphasis on the physiology underlying the mathematical expressions used. The model is tested and evaluated, using the Mean Squared Deviation method (MSD), to meet the objective of finding the strength and weaknesses of the model by comparing simulated and actual results of various aspects of growth and development of maize grown in Southern Ontario. The critical issue in testing the 'validity' of the model is not necessarily the "fit" of the model to a given data set, but rather its capability to solve a conceptual problem and the validity of the model's hypotheses and logical structure. The continuous flow of research results that appears in the literature should be used to improve crop models and, in doing so, the interpretation and impact of the very results will be enhanced. The question what information is most limiting the utility of the model is not only important to the crop modeler, but also points to high impact areas of research.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".