Electromagnetic Induction Sensor Data to Identify Areas of Manure Accumulation on a Feedlot Surface
Bibliographic record
Abstract
A study was initiated to test the validity of using electromagnetic induction (EMI) survey data, a prediction‐based sampling strategy, and ordinary linear regression modeling to predict spatially variable feedlot surface manure accumulation. A 30‐ by 60‐m feedlot pen with a central mound was selected for this study. A Dualem‐1S EMI meter (Dualem Inc., Milton, ON, Canada) pulled on 2‐m spacing was used to collect feedlot surface apparent electrical conductivity (EC a ) data. Meter data were combined with global positioning system coordinates at a rate of five readings per second. Two 20‐site sampling approaches were used to determine the validity of using EMI data for prediction‐based sampling. Soil samples were analyzed for volatile solids (VS), total N (TN), total P (TP), and Cl − A stratified random sampling (SRS) approach ( n = 20) was used as an independent set to test models estimated from the prediction‐based ( n = 20) response surface sample design (RSSD). The RSSD sampling plan demonstrated better design optimality criteria than the SRS approach. Excellent correlations between the EMI data and the ln(Cl − ), TN, TP, and VS soil properties suggest that it can be used to map spatially variable manure accumulations. Each model was capable of explaining >90% of the constituent sample variations. Fitted models were used to estimate average manure accumulation and predict spatial variations. The corresponding prediction maps show a pronounced pen design effect on manure accumulation. This technique enables researchers to develop precision practices to mitigate environmental contamination from beef feedlots.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".