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Record W2739872120 · doi:10.2134/cs2017.50.0311

Potential of bioenergy cropping systems for soil and water quality improvement

2017· article· en· W2739872120 on OpenAlexaff
Roger Nkoa Ondoua, Katie Kendall, Bill Deen

Bibliographic record

VenueCrops & Soils · 2017
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicBioenergy crop production and management
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsCroppingEnvironmental scienceAgronomyCrop rotationBioenergyAgricultureAgroforestryBiofuelWillowDryland farmingSoil qualityAgricultural engineeringSoil waterCropGeographyEngineeringSoil scienceBiologyBiotechnologyEcology

Abstract

fetched live from OpenAlex

The use of grains from row crops to produce bioethanol or biodiesels has had a negative impact on food prices. At the same time, systematic and complete removal of crop residues for biofuel production adversely impacts soil quality. This article discusses the seven‐year change in some of the soil chemical properties that affect soil quality, following the adoption of three land uses: corn–soybean rotation, C 4 warm‐season grasses, and short‐rotation willow farming. Earn 0.5 CEUs in Soil & Water Management by reading this article and taking the quiz at www.agronomy.org/education/classroom/classes/482

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.007
Threshold uncertainty score0.023

Distilled classifier scores by category (both heads)

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 communication0.0010.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0070.001

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 head0.030
GPT teacher head0.258
Teacher spread0.228 · 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 source (direct Gemma or distilled Codex), 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

Citations0
Published2017
Admission routes1
Has abstractyes

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