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Record W6940856099 · doi:10.1139/cjss2011-108

Retention and nitrification of injected anhydrous NH3 as affected by soil pH

2012· article· en· W6940856099 on OpenAlexaboutno aff

Bibliographic record

VenueBioOne Complete (BioOne) · 2012
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicMycorrhizal Fungi and Plant Interactions
Canadian institutionsnot available
Fundersnot available
KeywordsAnhydrousNitrificationSoil waterAmmoniaLeaching (pedology)Soil pHNitrogen

Abstract

fetched live from OpenAlex

Benke, M. B., Goh, T. B., Karamanos, R., Lupwayi, N. Z. and Hao, X. 2012. Retention and nitrification of injected anhydrous NH 3 as affected by soil pH. Can. J. Soil Sci. 92: 589-598. Anhydrous ammonia is an economical and extensively used fertilizer, yet loss after injection can reduce its agronomic efficiency. A laboratory experiment was conducted to examine how soil properties affect ammonia retention and nitrification following anhydrous NH3 injection using 10 different Canadian prairie soils. Soils were also injected with atmospheric air for comparison. Following injection, soils were incubated for up to 216 h at field capacity. Among the soil properties studied [pH (1:2 water), clay, total N, and organic C contents], only pH was negatively related (R 2=0.55, n=10, 24 h incubation) to percentage injected N retained by soil. The amount of N retained by soil 24 h following injection was 92±2% (mean±SEM) when pH <6, compared with 64±2% when pH>7.5. Rate of nitrification increased (P<0.001) about 48-96 h following injection and was greater in pH>7.5 than pH<6 soils. There was no difference (P>0.05) in bacterial diversity between ammonia- and air-injected soils. The slower nitrification rates suggest that potential leaching and denitrification losses in acid soils could be smaller than in alkaline soils.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.755
Threshold uncertainty score1.000

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)0.0010.000

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 head0.171
GPT teacher head0.220
Teacher spread0.049 · 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 designBench or experimental
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
Published2012
Admission routes1
Has abstractyes

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