Utilization of Object‐Oriented Software in the Image Analysis of Soil Thin Sections
Bibliographic record
Abstract
Computerized analysis of soil thin section imagery leads to quantitative estimation of various soil features and allows interpretation of soil properties in connection with soil genesis and evolution. Our objective was to assess an object‐oriented software (that was created especially for remote sensing) in the study of soil thin sections. The capabilities of this software were tested on three Orthic Humic Gleysols (Typic Humaquepts), profiles that have been thoroughly analyzed in the past by means of more traditional image analysis methods. The software utilizes a multiresolution segmentation function that enables the separation, at different levels of observation, of objects representing micromorphological features. Objects that corresponded to voids, coarse and fine fractions with variable compositions, and redoximorphic pedofeatures were distinguished and grouped in classes arranged in a hierarchical manner. Classification was based not only on object spectral values, but also on object shape characteristics, as well as class‐related features. Each designated class was evaluated quantitatively. Structural changes caused by agricultural practices were evidenced by image analysis in conjunction with micromorphological characterization. Relational and contextual data concerning Fe–Mn nodules and mottles were extracted and interpreted from the perspective of pedogenetic conditions.
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".