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Record W3201212460 · doi:10.32393/csme.2021.71

Analyzing Robustness Of Granular Agrochemicals: Basis For The Development Of A Pneumatic Spot Applicator

2021· article· en· W3201212460 on OpenAlexaff
Craig B. MacEachern, Travis J. Esau, Qamar Uz Zaman

Bibliographic record

VenueProgress in Canadian Mechanical Engineering. Volume 4 · 2021
Typearticle
Languageen
FieldEngineering
TopicAgricultural Engineering and Mechanization
Canadian institutionsDalhousie University
Fundersnot available
KeywordsRobustness (evolution)AgrochemicalComputer scienceBasis (linear algebra)Artificial intelligenceMathematicsBiology

Abstract

fetched live from OpenAlex

This methodology is part of a larger work to develop a granular agrochemical spot applicator for a variety of on farm applications.As part of the development, it is critical to analyze the robustness of the target agrochemical to ensure that the product does not breakdown when cycled pneumatically.Prior to this work their existed no such methodology for analyzing a granules ability to resist degradation resulting from pneumatic and impact stresses.To assess granule robustness, granules of three different agrochemicals (Casoron G-4, 9-30-11 MESZ granular fertilizer and clay filler) were cycled for one hour through 4.87 m of 31.75 mm inner diameter hose.Bulk densities of each agrochemical were recorded before and after cycling the product and used for comparison.If the product observed a significant increase in bulk density following cycling, then it can be stated that the granule was seeing significant degradation resulting from the stresses.Casoron G-4 did not see any significant degradation (p = 0.220) resulting from the cycling.Both the fertilizer and the clay filler did see significant breakdown (p < 0.001) resulting from the pneumatic and impact stresses.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.758
Threshold uncertainty score0.712

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)0.0000.000

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 head0.008
GPT teacher head0.202
Teacher spread0.194 · 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 designSimulation or modeling
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
Published2021
Admission routes1
Has abstractyes

Explore more

Same venueProgress in Canadian Mechanical Engineering. Volume 4Same topicAgricultural Engineering and MechanizationFrench-language works237,207