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Record W2064302503 · doi:10.1155/2010/132080

Fresh and Composted Paper Sludges Sustain Soil Productivity

2010· article· en· W2064302503 on OpenAlexaff
Adrien N’Dayegamiye, Anne Drapeau, Cargèle Nduwamungu

Bibliographic record

VenueInternational Journal of Agronomy · 2010
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicSoil Carbon and Nitrogen Dynamics
Canadian institutionsInstitut de Recherche et de Développement en Agroenvironnement
Fundersnot available
KeywordsSilageFertilizerAgronomyHordeum vulgareCropAnimal scienceChemistryEnvironmental scienceMathematicsPoaceaeBiology

Abstract

fetched live from OpenAlex

In this 5-yr study, application frequency of fresh (FDS) and composted (CDS) paper sludges was evaluated on soil properties and silage corn ( Zea mays L.) and barley ( Hordeum vulgare L.) yields and N uptake by comparing one application (2000) with repeated applications (2000, 2001, 2002). Treatments consisted of a control with neither amendment nor N fertilizer, a recommended N rate for corn (160 kg N ha −1 ) and barley (90 kg N ha −1 ), and FDS and CDS at a rate of 40 Mg ha −1 on wet basis with or without reduced N fertilizer (120 kg N ha −1 ) on corn. Compared to the control, the FDS or CDS applications significantly increased the proportions of soil larger macroaggregates (>2 mm) and the mean-weight diameter (MWD) of aggregates, regardless their application frequency. Single or three yrs additions of FDS or CDS produced, on average, an increase of 25 to 31% of the soil C and N contents compared to the control and the recommended N fertilizer treatment (160 kg N ha −1 ). In the first year of their application, the FDS or CDS with or without supplemental N fertilizer (120 kg N ha −1 ) reduced silage corn and N uptake, compared to the control probably due to N immobilization. However, silage corn yields were increased in the following years with one application or with repeated application of FDS or CDS. Results show that benefits on soil properties and crop yields of single application of FDS and CDS were similar to those of the triple applications and were still noticeable a few years following the last application.

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 score0.733
Threshold uncertainty score0.260

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 head0.007
GPT teacher head0.215
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

Citations4
Published2010
Admission routes1
Has abstractyes

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