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Record W4394303663 · doi:10.6084/m9.figshare.20443787

INFLUENCE OF EXCITATION POSITION ON MECHANIZED PICKING EFFECT OF CAMELLIA OLEIFERA

2022· dataset· en· W4394303663 on OpenAlexaff
Delin Wu, Enlong Zhao, Shan Jiang, Ding Da, Yangyang Liu

Bibliographic record

VenueFigshare · 2022
Typedataset
Languageen
FieldAgricultural and Biological Sciences
TopicPolysaccharides Composition and Applications
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsCamellia oleiferaExcitationPosition (finance)HorticultureChemistryEnvironmental scienceEngineeringBiologyElectrical engineeringBusiness

Abstract

fetched live from OpenAlex

ABSTRACT Camellia oleifera oil is easily absorbed by the human body and has a high development prospect in the research and development of new drugs. However, the labor intensity involved in picking has limited the development of the camellia oil industry. Vibratory mechanized harvesting is considered to be an effective way to solve harvesting difficulties. In this study, the whole process of accelerating the mechanical vibration picking is analyzed theoretically, according to different vibration positions (height), and a vibration picking experiment is carried out. The ratio of the optimal excitation location to the years of growth of Camellia oleifera was found to be between 16 and 20. It was observed that with the increase of camellia growing years, this ratio gradually decreased, and its optimal vibration position showed an increasing trend. Further, when the excitation time was greater than 8 s, the fruit removal rate did not continue to increase, but the buds and leaves falling continued to decrease. This study can effectively improve the efficiency of camellia oil fruit picking and reduce the drop-off of camellia buds and leaves.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.179
Threshold uncertainty score0.821

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)0.1800.000

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 head0.017
GPT teacher head0.250
Teacher spread0.233 · 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.

Study designNot applicable
Domainnot available
GenreDataset

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
Published2022
Admission routes1
Has abstractyes

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