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Record W1589243692 · doi:10.52151/jae2006431.1163

Performance Evaluation of Shovel Type Furrow Opener of a Seed cum Fertilizer Drill in Sandy Soils

2024· article· en· W1589243692 on OpenAlexaff
D.P. Darmora, Keshaw Prasad Pandey

Bibliographic record

VenueJournal of Agricultural Engineering (India) · 2024
Typearticle
Languageen
FieldEngineering
TopicSoil Mechanics and Vehicle Dynamics
Canadian institutionsAgriculture and Agri-Food Canada
Fundersnot available
KeywordsShovelDrillFertilizerSoil waterSoil typeGeologyAgricultural engineeringEnvironmental scienceAgronomySoil scienceEngineeringBiology

Abstract

fetched live from OpenAlex

A shovel type furrow opener having optimized values of rake angle, wedge angle, boot geometry and the seed fertilizer tube spacing was evaluated against standard shovel type furrow opener (BIS)under controlled soil bin condition in sandy loam and loamy sand soils. The performance evaluation was made on the basis of draft requirement, soil cover, lateral and vertical seed scatter, lateral and vertical separations maintained between the seed and fertilizer placements and row roughness coefficient at a soil moisture of 10 per cent (db) and an operating depth and speed of 60 mm and 0.562 ms-1. respectively. The furrow openers require about 18 per cent less draft and created less soil disturbance in comparison to standard shovel type furrow opener. The seed-fertilizer distribution ability of the furrow opener was also found better than the standard-shovel type furrow opener as it placed the seed and fertilizer at a lateral and vertical separations of about 33 mm and 21 mm, respectively against a negligible vertical separation maintained by the BIS-shovel type furrow opener. The value of overall performance index of the furrow opener was also much higher than the standard -shovel type furrow opener.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.311
Threshold uncertainty score0.450

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 science0.0000.000
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 head0.013
GPT teacher head0.225
Teacher spread0.212 · 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
Published2024
Admission routes1
Has abstractyes

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