Using cultivated organic soil depth to form soil conservation management zones
Bibliographic record
Abstract
Cultivated Organic soils in Montreal's southwest plain are the most productive soils in the province of Quebec. After their initial drainage to enable farming, Organic soils are susceptible to many forms of degradation and soil loss. In this study, we characterized the physical, chemical, and pedological properties of 114 sites from five peatlands to form soil conservation management zones. We attempted to use the maximum peat thickness (MPT) as a soil degradation proxy. The MPT can be defined as the thickness of the layer of peat until coprogenous or mineral materials are reached. The latter are undesired growing media and are not considered in MPT calculation. A series of multivariate analysis of variance indicated that MPT was moderately related to soil degradation (optimal model's Pillai's trace = 0.495). Three soil degradation groups were defined, separated by two MPT thresholds: 60 and 100 cm. When looking at 17 different depth-property combinations, shallower sites (MPT < 60 cm) showed signs of soil degradation significantly higher than sites with an MPT above 60 cm. The second threshold was proposed for practical purposes. Then, these thresholds were used to separate the study area into spatially distinct management zones. Important spatial contrasts were found. This supports the theory that precision agriculture techniques are needed to target fields to optimize soil conservation interventions. The relationship between the MPT and soil degradation should be further explored to account for other degradation factors, and to better identify degraded soils and soils at risk.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".