Estimating Earthworm‐Influenced Soil Structure by Morphometric Image Analysis
Bibliographic record
Abstract
Earthworms have a profound influence on soil processes. However, there is generally a lack of adequate means by which to assess the influence of earthworms on soil structure. Not until quantitative methods on undisturbed soil samples are developed will there be any adequate measure of the influence of earthworms on soil structure. This paper describes an extension of an image‐analysis method developed for the quantitative determination of the influence of earthworms on soil structural properties. Mammillated vughs are most likely developed by the burrowing of soil macrofauna, in particular earthworms. A learning set of mammillated vughs was compiled with pores taken from a soil developed solely through the channeling and casting of earthworms. This learning set was used to classify soil blocks taken from a no‐till and conventionally tilled treated soil. The results indicated that the no‐till soils had more than twice the number of mammillated vughs >1000 μm in diameter. This was attributed to the larger earthworm population in the no‐till soils, coupled with the change in morphology or destruction of some of the mammillated vugh features caused by disturbance in the conventionally tilled soil. This method should allow for a more effective means to evaluate the influence of earthworms on soil properties within any given soil profile.
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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 | 0.001 |
| 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 | 0.000 | 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".