Responses of forest snail assemblages to soil acidity buffer system and liming
Bibliographic record
Abstract
Abstract Physiological constraints from harsh environmental conditions, such as from calcium limitation on acidic soils, is expected not only to affect species richness, but also species abundance distributions. Also, the effects of amendments by calcium addition (soil liming) on these assemblage characteristics are poorly understood. Because of their sensitivity towards calcium availability, we use snails as model organisms and integrate field surveys and literature data. Temperate forest snail data supported a rule‐of‐thumb calibration with pH measurements in water being one unit higher than in KCl buffer. The resulting large data set suggests stepwise changes in snail richness that occur at transitions in soil buffer systems, especially at pH 3.2. Species abundance distributions follow the logseries model in most soil buffer systems, except for the iron buffer range (pH ≤3.2) where they swap to the geometric model. Our findings thus suggest several smaller soil pH thresholds for snail assemblages associated with shifts between soil buffer systems, and a tipping point at the threshold to pH ≤3.2. Liming with ground carbonate rocks is a technique to temporarily increase soil pH and calcium availability in forest soils, but its effects on snail assemblages produced inconsistent results that did not meet expectations from the ameliorated soil pH and might warrant a re‐evaluation of liming applications.
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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 | 0.000 | 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) | 0.001 | 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".