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Record W2010737607 · doi:10.1080/03601230701229155

Influence of crop residues on trifluralin mineralization in a silty clay loam soil

2007· article· en· W2010737607 on OpenAlexafffund
Annemieke Farenhorst

Bibliographic record

VenueJournal of Environmental Science and Health Part B · 2007
Typearticle
Languageen
FieldEnvironmental Science
TopicPesticide and Herbicide Environmental Studies
Canadian institutionsUniversity of Manitoba
FundersAustralian National UniversityManitoba Rural Adaptation Council
KeywordsTrifluralinLoamMineralization (soil science)AgronomySoil scienceEnvironmental scienceSoil waterBiologyPesticide

Abstract

fetched live from OpenAlex

Trifluralin is typically applied onto crop residues (trash, stubble) at the soil surface, or onto the bare soil surface after the incorporation of crop residues into the soil. The objective of this study was to quantify the effect of the type and amount of crop residues in soil on trifluralin mineralization in a Wellwood silty clay loam soil. Leaves and stubble of Potato (Solanum tuberosum) (P); Canola (Brassica napus) (C), Wheat (Triticum aestivum) (W), Oats (Avena sativa), (O), and Alfalfa (Medicago sativa) (A) were added to soil microcosms at rates of 2%, 4%, 8% and 16% of the total soil weight (25 g). The type and amount of crop residues in soil had little influence on the trifluralin first-order mineralization rate constant, which ranged from 3.57E-03 day(-1) in soil with 16% A to 2.89E-02 day(-1) in soil with 8% W. The cumulative trifluralin mineralization at 113 days ranged from 1.15% in soil with 16% P to 3.21% in soil with 4% C, again demonstrating that the observed differences across the treatments are not of agronomic or environmental importance.

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)0.003
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 score0.126
Threshold uncertainty score0.518

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.001
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 head0.024
GPT teacher head0.304
Teacher spread0.281 · 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
Published2007
Admission routes2
Has abstractyes

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Same venueJournal of Environmental Science and Health Part BSame topicPesticide and Herbicide Environmental StudiesFrench-language works237,207