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Record W7065145291

A Comparison of N<sub>2</sub>O Emissions After Application of Dairy Slurry on Perennial Grass or Bare Soil Prior to Planting an Annual Crop in Coastal British Columbia, Canada

2023· article· en· W7065145291 on OpenAlexaboutno aff

Bibliographic record

VenueUKnowledge (University of Kentucky) · 2023
Typearticle
Languageen
FieldPhysics and Astronomy
TopicMagnetic confinement fusion research
Canadian institutionsnot available
Fundersnot available
KeywordsManurePerennial plantSowingCropTillageSlurry
DOInot available

Abstract

fetched live from OpenAlex

Because of restrictions on land application of manure in autumn and winter, dairy farmers in coastal British Columbia (BC) must apply half of their annual manure supply from mid-Feb. to mid-April. Although two thirds of their land is in perennial grass, most of this manure is applied to bare soil, usually maize land, prior to planting. This is done for convenience and to avoid damaging grass stands with equipment traffic. Farmers are encouraged to allocate more manure to grass to minimise soil NO3 concentrations after maize harvest, because maize takes up less N than grass, and the bare fields after harvest are subject to wintertime leaching. However, the effect of this practice on emissions of N2O is not known. Our objective was to compare the effect of spring application of manure on bare land and on grass with respect to emissions of N2O. A second objective was to compare early, late and split applications of manure.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.496
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0050.000

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 head0.011
GPT teacher head0.243
Teacher spread0.232 · 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.

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
Published2023
Admission routes1
Has abstractyes

Explore more

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