Estimating Particle Density from Soil Inventory Data in the Lake Erie Lowlands
Bibliographic record
Abstract
The objective of this study was to develop a statistically based function for the estimation of particle density of texturally diverse soils sampled from a reasonably large geographic area (~12,000 km 2 ) to enhance further development of pedotransfer functions with regional application. Available soil physical property data were assembled for soil series mapped during five municipal‐level soil inventory upgrades in southwestern Ontario, Canada. A total of 282 soil horizons from 91 soil profiles were identified that had the requisite measured data for particle density (water pycnometer method), soil organic carbon content (wet oxidation method), and particle‐size distribution (pipette method). Both linear and nonlinear regression procedures were used to relate particle density to soil organic matter content. Plausible estimates of particle density for the mineral component (2.65 Mg m □3 ), and particularly for the humic component (1.23 Mg m ‐3 ), were obtained (r 2 = 0.208, RMSE = 0.11 Mg m □3 , P < 0.0001) even though the calibration data set had a limited range of soil organic matter content (<12%). The particle density of different mineral particle‐size fractions (e.g., clay) could also be distinguished statistically. The predictive capability of regression equations originating from soil inventory data sets encompassing large geographic areas are likely to be influenced by the soil taxonomic range sampled and the general data quality.
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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 | 0.000 |
| 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".