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Record W1255934516

Relative Importance of Physical Constraints on Decomposition

2011· article· en· W1255934516 on OpenAlexaboutno aff
Sara J. Klapstein

Bibliographic record

VenueBiochemistry and Molecular Biology Education · 2011
Typearticle
Languageen
FieldEngineering
TopicElectrokinetic Soil Remediation Techniques
Canadian institutionsnot available
Fundersnot available
KeywordsEnvironmental scienceSoil respirationDecompositionSoil carbonSoil scienceOrganic matterSoil waterCarbon cycleSoil organic matterEnvironmental chemistryChemistryEcologyEcosystem
DOInot available

Abstract

fetched live from OpenAlex

ð Soil organic matter (SOM) stability is thought to be dependent almost entirely on temperature, moisture, and microbial dynamics. While soil physical factors are also known determinants of SOM decomposition, proportionally little work has attempted to determine how these factors could regulate future rates of CO 2 release from soils under a changing climate. Here, paired lab-field experiments explore the effects of change in the physical environment and carbon dioxide (CO 2 ) respiration of SOM in mineral soil from an 80-year old red spruce forest stand in Nova Scotia, Canada. Factors tested were substrate transport, solubilization, oxygen availability, and physical structure, and were performed using the following respective disturbance methodologies: electrokinetics, wetting, air- sparging, and abrasion and compaction. Most treatments drove change in SOM decomposition rates, and the effect of the disturbance usually decayed over several days. Interestingly, laboratory and field results differed strongly, and opposite responses were often observed for a given type of disturbance. Electrokinetics, or the movement of substrates independent of other disturbances, did not produce any change to the soil CO 2 emission regime. Few studies have tackled the importance of physical controls on soil decomposition, but this is new and potentially important work for making accurate future predictions of terrestrial carbon cycling.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.014
Threshold uncertainty score0.251

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.005
GPT teacher head0.263
Teacher spread0.258 · 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 designBench or experimental
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

Citations0
Published2011
Admission routes1
Has abstractyes

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