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Record W2061697377 · doi:10.2134/agronj2004.0997

Land Application of Oily Food Waste and Corn Production on Amended Soils

2004· article· en· W2061697377 on OpenAlexaff
M. T. Rashid, R. P. Voroney

Bibliographic record

VenueAgronomy Journal · 2004
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicSoil Carbon and Nitrogen Dynamics
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsLoamSoil waterAgronomyEnvironmental scienceGreaseCropChemistrySoil scienceBiology

Abstract

fetched live from OpenAlex

Oily food waste containing high concentrations of fat, oil, and grease (FOG) is produced by the food service and food production/processing industries. Fat, oil, and grease have a high C/N ratio (90:1) and, if applied to agricultural soils, may affect the availability of N to crops, due to soil N immobilization during its decomposition. Several field experiments were conducted on silt loam and loam soils (fine‐loamy mixed, mesic Glossoboric Hapludalfs) from 1995–1997 to determine: (i) effect of FOG on corn ( Zea mays L.) crop yields, (ii) N requirements of corn grown on FOG‐amended soils, (iii) contribution to soil C, and (iv) its accumulation in soil after continuous application. Corn grain yields were maintained with FOG applied at 5, 10, and 15 Mg ha −1 , providing that sufficient N was available to fulfill the needs of soil microorganisms during FOG decomposition and crop growth. The N rates ranged from 170 to 510 kg N ha −1 for these FOG application rates. Soil organic C was significantly increased by 9 and 19% with the continuous FOG application for 3 yr at 10 and 15 Mg FOG ha −1 yr −1 . The residual FOG contents after 3 yr at these application rates were <2% of the total FOG applied with no expected FOG buildup in soil. Corn grain yields were not affected by FOG application in fall and were equal to control plots during 1996 and 1997. However, corn grain yields were decreased by FOG applied in spring, and supplemental N (62 and 59 kg N ha −1 in 1996 and 1997, respectively) was required to maintain yields similar to control plots.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.627
Threshold uncertainty score0.100

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 head0.013
GPT teacher head0.200
Teacher spread0.187 · 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

Citations28
Published2004
Admission routes1
Has abstractyes

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