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Record W2112952557 · doi:10.4141/s04-049

Estimating carbon retention in soils amended with composted beef cattle manure

2005· article· en· W2112952557 on OpenAlexaffvenue
Bobbi L. Helgason, Francis J. Larney, H. H. Janzen

Bibliographic record

VenueCanadian Journal of Soil Science · 2005
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicComposting and Vermicomposting Techniques
Canadian institutionsLethbridge CollegeAgriculture and Agri-Food Canada
Fundersnot available
KeywordsCompostAmendmentLoamManureAgronomySoil waterMineralization (soil science)ChemistryNutrientSoil conditionerSoil typeEnvironmental scienceSoil scienceBiology

Abstract

fetched live from OpenAlex

Composted cattle manure is often used as a soil amendment to replenish nutrient pools and to supply a source of stable C. Compost composition affects the availability of nutrients and the stability of C following the addition of compost to soil. We investigated C mineralization in a loamy sand and a loam soil amended with nine composts, two fresh manures and alfalfa (Medicago sativa L.) hay at a target rate of 10 mg total C g -1 soil. Soils were incubated at 25°C for 168 d. There was a significant interaction between amendment and soil type on C mineralization but generally, the effect of soil texture on amendment decomposition was small. The composts were very dissimilar in composition and resulted in substantial differences in the amount of C retained in the soils (2-39% C added evolved as CO 2 ). Total C evolved during the incubation period could be predicted from the NH 4 -N content and the NH 4 -N/NO 3 -N ratio of the composted manures (R 2 = 0.91–0.93). Estimation of the C retained in soils amended with compost as a function of simple chemical properties of the compost provides an important tool for evaluating the effectiveness of compost as a soil amendment, helping to calculate net retention of C. Key words: Compost, mineralization, soil carbon, amendment

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.581
Threshold uncertainty score0.979

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.022
GPT teacher head0.230
Teacher spread0.208 · 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

Citations29
Published2005
Admission routes2
Has abstractyes

Explore more

Same venueCanadian Journal of Soil ScienceSame topicComposting and Vermicomposting TechniquesFrench-language works237,207