Analyzing and Projecting Soil Moisture and Cone Penetrability Variations in Forest Soils
Bibliographic record
Abstract
This article details how forest soil moisture content (MC) and subsequent resistances to cone penetration (referred below as Cone Index, CI) vary by daily weather, season, topography, site and soil properties across eleven harvest blocks in northwestern New Brunswick. The MC- and CI-affecting soil variables refer to density, texture, organic matter content, coarse fragment content, and topographic position (i.e., elevation, and the seasonally affected cartographic depth-to-water (DTW) pattern). The harvest blocks were transect-sampled inside and outside their wood-forwarding tracks at varying times throughout the year. In detail, 61% of the pore-filled moisture content (MCPS) determinations inside and outside the tracks could be related to topographic position, coarse fragments, bulk density, and forest cover type specifications. In addition, 40% of the CI variations could be related to soil depth, MCPS, and block-specific cover type. Actual versus model-projected uncertainties amounted to ΔMCPS ≤ ± 15% and ΔCI ≤ ± 0.5 MPa, 8 times out of 10. Block-centered MC and CI projections were obtained through: 1) daily hydrological modelling using daily precipitation and air temperature weather-station records nearest each block, and 2) digitally mapped variations in soil properties, elevation, DTW and forest cover type, done at 10 m resolution.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".