MétaCan
Menu
Back to cohort
Record W2562546261 · doi:10.25165/ijabe.v9i6.1923

Design and experiment of a bionic vibratory subsoiler for banana fields in southern China

2016· article· en· W2562546261 on OpenAlex
Xirui Zhang, Chao Wang, Zhishui Chen, Zhiwei Zeng

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueInternational journal of agricultural and biological engineering · 2016
Typearticle
Languageen
FieldEngineering
TopicSoil Mechanics and Vehicle Dynamics
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsBionicsEngineeringAgricultural machineryMathematicsCrankAgricultural engineeringAgricultureMechanical engineeringGeography

Abstract

fetched live from OpenAlex

Abstract: Subsoiling is essential in the tillage of banana planting, as banana plants have a fairly sturdy pseudostem and wide row spacing while soil tends to be compacted. In this study, a bionic vibrating subsoiler for banana fields was developed, verified, and evaluated. The vibrator was designed based on crank-rocker mechanism while the bionics design was used for subsoiler development. The forces on the susboiler were analyzed to verify the strength of the subsoiler tine. To test the performance of the subsoiler, field tests were conducted to measure the draft force and fuel consumption. There was approximately 14% reduction in the draft force and 22% increase in the fuel consumption in vibrating mode compared with that in non-vibrating mode. In conclusion, the study results could be applied in China’s tropical agricultural regions. Keywords: vibratory subsoiler, tillage, simulation, bionics, banana field DOI: 10.3965/j.ijabe.20160906.1923 Citation: Zhang X R, Wang C, Chen Z H, Zeng Z W. Design and experiment of a bionic vibratory subsoiler for banana fields in southern China. Int J Agric & Biol Eng, 2016; 9(6): 75-83.

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.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.783
Threshold uncertainty score0.127

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)0.0000.000

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 head0.011
GPT teacher head0.196
Teacher spread0.185 · 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