Development of Pedotransfer Functions to Predict Soil Physical Properties in Southern Quebec (Canada)
Bibliographic record
Abstract
Pedotransfer functions (PTFs) are empirical fits to soil property data and have been used as an alternative tool to in situ measurements for estimating soil hydraulic properties for the last few decades. PTFs of Saxton and Rawls, 2006 (PTFs’S&R.2006) are some of the most widely used because of their global aspect. However, empirical functions yield more accurate results when trained locally. This study proposes a set of agricultural PTFs developed for southern Quebec, Canada for three horizons (A, B, and C). Four response variables (bulk density (ρb), saturated hydraulic conductivity (Ksat), volumetric water content at field capacity (θ33), and permanent wilting point (θ1500)) and four predictors (clay, silt, organic carbon, and coarse fragment percentages) were used in this modeling process. The new PTFs were trained using the stepwise forward regression (SFR) and canonical correlation analysis (CCA) algorithms. The CCA- and SFR-PTFs were in most cases more accurate. Θ1500 and at θ33 estimates were improved with the SFR. The ρb in the A horizon was moderately estimated by the PTFs’S&R.2006, while the CCA- and SFR-PTFs performed equally well for the B and C horizons, yet qualified weak. However, for all PTFs for all horizons, Ksat estimates were unacceptable. Estimation of ρb and Ksat could be improved by considering other morphological predictors (soil structure, drainage information, etc.).
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 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 | 0.000 |
| 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".