Filter for penetration resistance correction in cultivated and compacted Histosols
Bibliographic record
Abstract
Abstract The objective of this study was to design, using field data, a filter to correct the soil penetration resistance (PR) values by considering all the effects (penetration friction component [PFC], varying penetration rate, and shocks) that bias PR interpretation. The data used were collected in cultivated peatlands of Montérégie (Canada) known for their advanced state of compaction. Penetration resistance (standard measurements) and cone resistance (CR) measurements (successive measurements per 10‐cm layer) were carried out on four peatlands (five stations per peatland). The filter components, a multi‐resolution analysis (AMR), a polynomial model, and a quantification and removal of PFC were identified and were used to design the filter and tested by cross‐validation. Our study showed that PFC exists ( p value < 5%) and does not depend on the peatland on which the measurements are made. An average filter has therefore been designed for the entire site and its effectiveness has been demonstrated. An application to the data collected in 10 fields on the site made it possible to identify the main soil density profiles existing in Montérégie and to quantify the average PFC characterizing these soils. We noticed that, on average, PFC represents between 18.6 and 41% of the PR value. The combined results of the statistical and physical analyses of the filter made it possible to recommend degrees 2 (D2), 3 (D3), and 5 (D5) of the polynomial model to adjust density profiles of cultivated Histosols when there are no, one, or two compact layers.
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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 | 0.000 | 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".