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Record W2082155909 · doi:10.1007/s11284-014-1167-9

Responses of forest snail assemblages to soil acidity buffer system and liming

2014· article· en· W2082155909 on OpenAlexfundno aff
Heike Kappes, Werner Topp

Bibliographic record

VenueEcological Research · 2014
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicMollusks and Parasites Studies
Canadian institutionsnot available
FundersCardiff UniversityMcGill University
KeywordsSoil pHSpecies richnessSoil waterSnailEnvironmental scienceEcologySoil acidificationTemperate climateTemperate forestAbundance (ecology)Soil scienceBiology

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.174
Threshold uncertainty score0.329

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.138
GPT teacher head0.358
Teacher spread0.220 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations6
Published2014
Admission routes1
Has abstractyes

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