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Peer Review #3 of "Manure application increased denitrifying gene abundance in a drip-irrigated cotton field (v0.1)"

2019· peer-review· en· W4242130013 on OpenAlexaff

Bibliographic record

Venuenot available
Typepeer-review
Languageen
FieldMaterials Science
TopicMetallurgy and Material Science
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsDenitrifying bacteriaDrip irrigationAbundance (ecology)Environmental scienceAgronomyManureDenitrificationBiologyNitrogenChemistryEcologyIrrigation

Abstract

fetched live from OpenAlex

Application of inorganic nitrogen (N) fertilizer and manure can increase nitrous oxide (N 2 O) emissions.We tested the hypothesis that increase in N 2 O flux from soils amended with manure reflects a change in bacterial community structure and, specifically, an increase in the number of denitrifiers.To test this hypothesis, a field experiment was conducted in a drip-irrigated cotton field in an arid region of northwestern China.Treatments included plots that were not amended (Control), and plots amended with urea (Urea), animal manure (Manure) and a 50/50 mix of urea and manure (U+M).Manure was broadcastincorporated into the soil before seeding while urea was split-applied with drip irrigation (fertigation) over the growing season.The addition treatments did not, as assessed by nextgen sequencing of PCR-amplicons generated from rRNA genes in soil, affect the alpha diversity of bacterial communities but did change the beta diversity.Compared to the Control, the addition of manure (U+M and Manure) significantly increased the abundance of genes associated with nitrate reduction (narG) and denitrfication (nirK and nosZ).Manure addition (U+M and Manure) did not affect the nitrifying enzyme activity (NEA) of soil but resulted in 39-59 times greater denitrifying enzyme activity (DEA).In contrast, urea application had no impact on the abundances of nitrifier and denitrifier genes, DEA and NEA; likely due to a limitation of C availability.DEA was highly correlated (r = 0.70 -0.84, P < 0.01) with the abundance of genes narG, nirK and nosZ.An increase in the abundance of these functional genes was further correlated with soil NO 3 -, dissolved organic carbon, total C, and total N concentrations, and soil C:N ratio.These results demonstrated a positive relationship between the abundances of denitrifying functional genes (narG, nirK, and nosZ) and denitrification potential, suggesting that manure application increased N 2 O emission by increasing denitrification and the population of

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)0.005
metaresearch head score (Gemma)0.022
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: Evaluation · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.995
Threshold uncertainty score0.568

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.022
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0040.002
Science and technology studies0.0030.001
Scholarly communication0.0030.002
Open science0.0020.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.1700.064

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 head0.321
Teacher spread0.290 · 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.

Study designNot applicable
DomainEvaluation
GenreOther

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".

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Citations0
Published2019
Admission routes1
Has abstractyes

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