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Record W2059335903 · doi:10.4141/s06-064

Residual soil nitrogen indicator for agricultural land in Canada

2007· article· en· W2059335903 on OpenAlexvenueaboutno aff
C. F. Drury, Jing Yang, R. De Jong, X.M. Yang, E.C. Huffman, V. Kirkwood, Keith Reid

Bibliographic record

VenueCanadian Journal of Soil Science · 2007
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicSoil Carbon and Nitrogen Dynamics
Canadian institutionsnot available
Fundersnot available
KeywordsNitrogenAgronomyEnvironmental scienceDenitrificationMineralization (soil science)NitrateFertilizerNitrogen fixationGrowing seasonCropManureNitrogen balanceCrop residueAgricultureChemistrySoil waterEcologySoil scienceBiology

Abstract

fetched live from OpenAlex

Residual soil nitrogen (RSN) is the amount of inorganic nitrogen that remains in the soil at the end of the growing season after crops have been harvested. RSN is an estimate of this quantity, calculated as the difference between all N inputs (fertilizer, manure-N, biological fixation, and atmospheric deposition) and all N outputs (N removed in crop harvest, N lost from ammonia volatilization and N lost from denitrification) assuming that mineralization and immobilization are generally balanced. RSN was calculated on a soil polygon level (scale 1:1 million) as well as on provincial and national levels for each of the 5 census years from 1981 to 2001. The Canadian average RSN values from 1981 to 1996 were fairly constant with a range of 12.9 to 13.9 kg N ha -1 . However, RSN increased by 51% from 13.9 kg N ha -1 in 1996 to 21.0 kg N ha -1 in 2001. This dramatic increase was due to several factors including an increase in legume crop acreage (i.e., increased biological N 2 fixation) and lower crop yields and reduced N uptake as a result of climatic constraints (droughts) which were prevalent in many regions in Canada in 2001. Key words: Nitrate, residual nitrogen, nitrogen balance, N indicator

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 score0.034
Threshold uncertainty score0.648

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
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 head0.011
GPT teacher head0.199
Teacher spread0.188 · 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

Citations45
Published2007
Admission routes2
Has abstractyes

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