Soil fertility characterization in podzolic soils of western Newfoundland using electromagnetic induction (EMI) sensors
Bibliographic record
Abstract
This study was conducted to assess the possibility of predicting soil fertility properties in western Newfoundland using multi-coil (MC) and multi-frequency (MF) electromagnetic induction (EMI) sensors. Two studies on podzolic boreal soils evaluated the effectiveness of apparent electrical conductivity (ECa) and apparent magnetic susceptibility (MSa) from both MC and MF EMI sensors in characterizing available nitrogen (N), phosphorus (P) and soil organic matter (SOM) as significant soil fertility properties. The first study evaluated the effectiveness of MC and MF-EMI sensors to estimate soil ammonium (NH4+), nitrate (NO3-) and orthophosphate (PO43-) using ECa as a proxy under two different land uses. The second study assessed the effectiveness of MSa obtained from the MC and MF-EMI sensors to estimate and map SOM using a statistical and geostatistical analysis. The first study revealed the potential application of EMI ECa as a proxy to assess the spatial variability of NH4+, NO3- and PO43- within a narrow range of concentrations for a specific site. A comparison between statistical and geostatistical analysis in the second study suggested that cokriging of SOM with densely sampled EMI MSa could provide higher accuracy in estimating and mapping SOM in podzolic soil. Both studies revealed that multiple linear regression models were more effective with the inclusion of soil water than simple linear regression models in predicting soil nutrients and SOM. However, the MC EMI sensor provided a better estimation of soil nutrients, whereas the MF-EMI sensor predicted SOM more effectively. The overall findings from this study demonstrated the potential of EMI sensors as a more accurate and robust method than conventional methods to evaluate and map soil fertility properties in boreal podzolic soils. Further studies are required to assess the potential of EMI sensors under different crop types, management practices, and moisture regimes to estimate and map soil fertility properties for podzolic soils.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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 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".