Application of multivariate adaptive regression splines (MARS) in precision agriculture
Bibliographic record
Abstract
Water quality is a major concern. Some agricultural practices can contribute to the degradation of water quality, particularly when fertilizers are not used efficiently. In order to properly manage nitrogen in corn production systems, the factors that govern yield must be identified. The multivariate adaptive regression spline (MARS) automated regression data mining method was used to determine the environmental factors that governed corn yield for cash and livestock cropping systems on clay loam soils in eastern Ontario, Canada during low yielding conditions in 2000. Statistically important variables included post-harvest soil water content, cone penetration resistance, and to a lesser degree, elevation and total mineral soil N (NH4+ + NO3-) in spring prior to planting. The MARS approach was deemed an acceptable, although time consuming approach for: identifying interactions between potentially yield governing variables/indicators, elucidating potential cause-effect processes, and identifying areas where soil physical constraints were potentially more important than soil nitrogen in governing yield.
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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 | 0.001 | 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".