Assessment of potential nutrient build-up around beef cattle production areas using electromagnetic induction
Bibliographic record
Abstract
Electromagnetic induction (EMI) has been used to map soil properties such as salinity and water content. The objective of this research is to use EMI to map the potential distribution of nutrients around beef cattle pens and to relate this distribution to major physiographic field features. Beef cattle farms in different physiographic locations were surveyed in Manitoba, Canada, using an EM-38 conductivity meter georeferenced with a GPS receiver. Samples were collected using a response surface design and analysed for electrical conductivity (ECe), which was used as a proxy for determining potential build-up of nutrients. Multiple linear regression models (MLR) were used for calibration of the EM readings. The results showed that areas 1 through 4 had ECe < or = 3.5 dSm(-1), but areas 5 and 6 exceeded this concentration and reached maximum values of 5.5 and 7.0 dS m(-1), respectively. Higher values in area 6 were probably due to the presence of a rocky layer at 0.3 m depth, leaving a thin soil layer to accumulate the nutrients. Micro-depressions played a major role in salt accumulation, with the depressions corresponding to higher values of ECe. The presence of features such as drainage ditches and compacted soils beneath roads strongly affected the direction of the plumes. Based on these results, the location of the pens on high elevations and the provision to collect the run-off from the pens were identified as good design criteria. Highly permeable soils may require a low permeability liner to capture the deep percolation and redirect it towards a collection area.
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".