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Record W2028174049 · doi:10.1080/00103620009370484

Nitrous oxide emissions from soil amended with glucose, alfalfa, or corn residues

2000· article· en· W2028174049 on OpenAlexaff
Mei Lin Shelp, E. G. Beauchamp, G. W. Thurtell

Bibliographic record

VenueCommunications in Soil Science and Plant Analysis · 2000
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicSoil Carbon and Nitrogen Dynamics
Canadian institutionsUniversity of GuelphWestern University
Fundersnot available
KeywordsNitrous oxideChemistryAgronomyDenitrificationIncubationNitrificationCrop residueNitrogenSoil waterEnvironmental chemistryEnvironmental scienceBiologySoil scienceEcology

Abstract

fetched live from OpenAlex

Abstract Nitrous oxide (N2O) emissions result from the nitrification and denitrification processes, the latter strongly affected by soil organic carbon (C) derived from plant residues. This study addressed two questions: (1) does plant residue C become less available to denitrifiers after a period of aerobic incubation, and (2) do plant residues with smaller particle sizes provide C for higher rates of N2O production due to a faster decomposition rate? Nitrous oxide fluxes from soil amended with alfalfa or corn residues, or glucose were measured in the laboratory using a gas flow‐through chamber system. Soil amended with these C substrates was also subjected to a 5‐d aerobic preincubation treatment. The significance of particle size on C availability was studied by comparing N2O released from soil amended with ground (particle size <1 mm) and large pieces (5‐cm lengths) of alfalfa residues. A 5‐d aerobic preincubation of soil amended with plant residues resulted in reduced N2O production during a subsequent anaerobic period. Results suggested that, due to consumption of the most available substrate, remaining C in plant residues is less available to denitrifiers after a period of aerobic incubation. Higher N2O losses were found with large alfalfa particles than with ground alfalfa.

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 score0.360
Threshold uncertainty score0.985

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.004
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.031
GPT teacher head0.254
Teacher spread0.223 · 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

Citations35
Published2000
Admission routes1
Has abstractyes

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