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Record W6921998000 · doi:10.1139/cjss2011-003

Effect of subsoiling and injection of pelletized organic matter on soil quality and productivity

2012· article· en· W6921998000 on OpenAlexaboutno aff

Bibliographic record

VenueBioOne Complete (BioOne) · 2012
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicSoil Carbon and Nitrogen Dynamics
Canadian institutionsnot available
Fundersnot available
KeywordsLoamOrganic matterBulk densitySoil waterSubsoilSoil compactionSoil organic matterCompactionPellets

Abstract

fetched live from OpenAlex

Leskiw, L. A., Welsh, C. M. and Zeleke, T. B. 2012. Effect of subsoiling and injection of pelletized organic matter on soil quality and productivity. Can. J. Soil Sci. 92: 269-276. Subsoil compaction is a widespread problem in most reclamation and other industrial operations. The objective of our research was to evaluate effectiveness of coupling deep subsoiling with injection of 20 Mg ha-1 organic matter pellets. Research was conducted at seven sites on a pipeline right-of-way in central Alberta. Treatments were subsoiling, subsoiling with pellets and a compacted right-of-way (control), established in spring and fall 2009. Treatment effects on soil physical properties and nutrient status were assessed in fall 2009 for spring-established sites and on all sites in fall 2010. Density and height of canola plants were determined in late summer 2010. Relative to the control, subsoiling with pellet treatments had lower bulk density in the 20- to 40-cm depth interval (up to 40%) in 2010, particularly in clay-loam soils. This treatment often had higher available phosphorus and total organic carbon in 2010, and total nitrogen in spring treated sites in 2009. Relative to the control, subsoiling with pellets had 46% higher canola plant density in clay loam soils of fall-treated sites. Subsoiling with pellets is recommended on heavy-textured, compacted soils to alleviate compaction and increase plant productivity.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.643
Threshold uncertainty score0.302

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.174
GPT teacher head0.252
Teacher spread0.078 · 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

Citations0
Published2012
Admission routes1
Has abstractyes

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