Evaluation of Soil Health Indicators at Long-Term Agricultural Sites in Ontario, Canada, and Indicator Prediction with Infrared Spectroscopy
Bibliographic record
Abstract
Four trials across Ontario were sampled to evaluate the effects of best management practices (BMP) on six soil health indicators (SHI). As well, the use of near infrared (NIR) and mid infrared (MIR) spectroscopy to predict SHI was explored at site and regional levels. Lastly, MIR spectroscopy was utilized to identify differences in soils under differing BMPs. The study found no-tillage (NT) improved SHI values at two of three sites. Inclusion of cover crops and small grains in rotation improved SHI values at one site, while no significant differences were found at two sites. The study found that MIR models predicted SHI better than NIR models. As well, the use of competitive adaptive reweighted sampling (CARS) feature selection improved model performance compared to full spectra models. Lastly, differences were found between MIR spectra from soils under NT and moldboard plow treatments and between annual versus perennial crop rotations.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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 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".