Calibration models for electromagnetic induction methods to assess nutrient accumulation beneath confined livestock areas
Bibliographic record
Abstract
Nutrient accumulation in soils beneath confined livestock areas is a potential source of groundwater contamination. Electromagnetic induction (EMI) has become a practical method to assess nutrient content, with multiple linear regression (MLR) as the statistical method often employed to translate EMI readings into nutrient content. The purpose of this research is to compare and contrast the performance of spatially referenced MLR models that include secondary, 'easy-to-acquire' predictor variables such as spatial coordinate locations, soil water content and elevation information with MLR models based solely on EMI readings. Six feedlot areas were surveyed with an EM38 conductivity meter and between 6 and 12 sites at each feedlot were sampled at five different depths. The electrical conductivity (EC(e)), nitrate (NO3-) and phosphate (PO4(3-)) concentrations were measured and used as response variables. Analyses were performed using two different approaches: the response variables in individual layers and response variables by combining the layers within the soil profile. The results of both MLR methods were comparable in most instances because the models preferentially incorporated predictors derived from EM38 readings. Differences between the models were more evident when predicting NO3- and PO4(3-), even though prediction of these two analytes by either method was generally poor. Combined profile analysis was more effective for defining nutrient build-up because by-layer analysis gave non-significant or poor models in many instances.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".