Quantification of soil volumes in the Eg & Bt-horizon of an Albeluvisol using image analysis
Bibliographic record
Abstract
In this study, we provide a strategy to quantify the effects on soil evolution of driving forces such as human activities or global change. This strategy was developed for situations in which soil evolution resulted in the formation of a complex juxtaposition of soil volumes with distinct properties including soil colours. It is based on image analysis. Our approach proceeds in two steps: (1) to find the minimum sample size over which the soil anisotropy can be neglected and (2) to define a Representative Elementary Volume (REV) of that sample. This approach was developed on the Eg & Bt horizon of a drained Albeluvisol in which three decimetric soil monoliths were sampled at 60, 110 and 210 cm from a drain. The monoliths were sliced into 1.5-cm horizontal layers. Each slice was photographed and studied by image analysis. At the monolith scale, there was neither lateral nor vertical anisotropy. The sampled monoliths were larger than the REV allowing quantification of the different soil volumes constituting this particular horizon. We quantified significant evolutions of the abundance of the different soil volumes characterized by their colour as a function of the distance to the drain. Such a quantification of the effects on soil evolution of human activities or global change equally applies for Podzols, Calcisols or Gleysols for which pedogenesis also resulted in contrasted soil colour evolutions. Key words: Soil change, pedogenesis quantification, artificial drainage, image analysis, Albeluvisol, representative elementary volume
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 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 | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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 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".