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Record W2013575062 · doi:10.4141/p99-004

Farm-level profitability analysis of alternative tillage systems on clay soils

2000· article· en· W2013575062 on OpenAlexvenueaboutno aff
Emmanuel K. Yiridoe, Tony J. Vyn, Alfons Weersink, David C. Hooker, Clarence J. Swanton

Bibliographic record

VenueCanadian Journal of Plant Science · 2000
Typearticle
Languageen
FieldEnvironmental Science
TopicSoil and Water Nutrient Dynamics
Canadian institutionsnot available
Fundersnot available
KeywordsTillageAgronomyConventional tillageMathematicsCrop rotationCropping systemMultiple croppingSoil waterStrip-tillCrop yieldCroppingEnvironmental scienceNo-till farmingCropAgricultureBiologySoil fertilitySowingSoil science

Abstract

fetched live from OpenAlex

Crop yields, production costs, and net returns for seven alternative conservation tillage (including five reduced tillage and two no-till) treatments were compared with a conventional tillage (CT) treatment for a corn (Zea mays L.) – soybean (Glycine max L. Merr) rotation for two clay soils in southwestern Ontario. There was no significant difference in corn and soybean yields between the CT and the conservation tillage systems at either location, although actual yields tended to be highest for CT management. Tillage treatments that used a common set of machinery for corn and soybean production generated savings in annual machinery costs. Variable costs were lowest for the reduced-tillage treatments and highest for the two no-till treatments due to higher equipment expenditures and additional herbicide requirements. For a given location, there was often no significant difference in net returns between CT and the conservation tillage treatments, although average net returns for the corn–soybean cropping systems tended to be highest for CT and lowest for no-till. Key words: Net returns analysis, conservation tillage systems, clay soils, corn, soybeans

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)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.061
Threshold uncertainty score0.121

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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 head0.019
GPT teacher head0.220
Teacher spread0.201 · 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.

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

Citations15
Published2000
Admission routes2
Has abstractyes

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