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Record W1987316469 · doi:10.4141/s03-045

Enzyme activity and C and N pools in soil following application of mulches

2004· article· en· W1987316469 on OpenAlexvenueno aff
Grazia Masciandaro, B. Ceccanti, Sergio Benedicto, H. C. Lee, Hadrian Cook

Bibliographic record

VenueCanadian Journal of Soil Science · 2004
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicSoil Carbon and Nitrogen Dynamics
Canadian institutionsnot available
Fundersnot available
KeywordsMulchCompostAgronomyStrawChemistryUreaseNutrientBiomass (ecology)Soil testSoil carbonForageSoil waterEnvironmental scienceBiologyEnzymeSoil scienceBiochemistry

Abstract

fetched live from OpenAlex

A field experiment was carried out to evaluate the effects of some mulching materials on soil biochemical properties that represent a component of soil quality and functionality. Wheat straw, dairy manure compost, and black polyethylene were used in the mulching experiments of a soil seeded with forage maize. Soil samples were taken from the surface (0–10 cm) and subsurface (10–20 cm) layers 1 and 4 mo after treatments were applied. Soil productivity, biochemical and chemical properties were determined. Compost treatment showed the highest agronomic yield due to the release of nutrients for plant nutrition and soil metabolism. Dehydrogenase activity was used as a measurement of overall microbial activity, whereas β-glucosidase and urease hydrolase activities were determined as an expression of C and N cycles. In addition, a ratio between dehydrogenase activity and water soluble carbon (defined as “metabolic potential index”) was calculated, in order to better represent soil metabolic potential. Compost-mulch treatment showed the lowest ratio values, while straw mulch had the highest value. A similar trend was found for β-glucosidase activity. The data confirmed the validity and sensitivity of the metabolic potential index and β-glucosidase enzyme in monitoring the change of soil biochemical properties and of soil quality during soil management practices. Key words: Soil enzyme activities, mulching practice, soil quality

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)0.001
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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.578
Threshold uncertainty score0.930

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.012
GPT teacher head0.213
Teacher spread0.200 · 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

Citations85
Published2004
Admission routes1
Has abstractyes

Explore more

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