Predicting Soil Properties from Organic Matter Content following Mechanical Site Preparation of Forest Soils
Bibliographic record
Abstract
The difficulties of sampling forest soils and their high spatial variability make estimation of soil physical properties following forest operations laborious. To develop prediction tools, soils were sampled from two sites located in the boreal forest of northern Québec, Canada. Soil organic matter (OM) content was found to be closely related to bulk density ( D b ) and porosity after clearcutting and mechanical site preparation (MSP) on these sites. Reasonably good estimates of D b , with an average error of 18 to 20%, can be made from the easily measurable OM concentration and the logarithmic relationships ( R 2 = 0.731 and 0.847, respectively for the Alma and Chibougamau sites) developed in this study. The organic density approach, recently developed for forest soils in New England, was found to be less precise ( R 2 = 0.637) than the logarithmic relationships following soil disturbance. For the two sandy till soils in northern Québec, the equation based on this concept best fit the data with a pure OM bulk density ( D bo ) of 0.159 Mg m −3 and a pure mineral matter bulk density ( D bm ) of 1.561 Mg m −3 The equations presented in this study also explain between 60 and 70% of the variation in porosity and C/N ratio from OM concentration, with prediction errors of 13 and 24%, respectively. In spite of soil surface disturbance associated with MSP, the easily measurable OM concentration can be used to predict D b , porosity, and C/N ratio.
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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 | 0.000 | 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 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".