MétaCan
Menu
Back to cohort
Record W2910499939 · doi:10.5937/savpoljteh1603143p

Technical and technological parameters cucumber pickles harvesting and processing

2016· article· en· W2910499939 on OpenAlexaff
Ondrej Ponjičan, Aleksandar Sedlar, Vladimir Višacki, Nenad Stanić

Bibliographic record

VenueSavremena poljoprivredna tehnika · 2016
Typearticle
Languageen
FieldEngineering
TopicAgricultural Engineering and Mechanization
Canadian institutionsDow Chemical (Canada)
FundersMinistarstvo Prosvete, Nauke i Tehnološkog Razvoja
KeywordsTractorProductivityYield (engineering)Work (physics)Agricultural engineeringAgricultural scienceMathematicsEnvironmental scienceEngineeringMechanical engineeringEconomicsPhysics

Abstract

fetched live from OpenAlex

Examination of technical and technological parameters during the harvesting and processing of cucumber pickles was performed in the exploitation conditions in Gospođinci. The area under cucumber pickles was 8.82 ha, with a yield of 124 t/ha. On the listed area involved two tractor units with carriage platforms for semi mechanized harvesting. Working width platform was 21 m and there in the lying position were 24 workers and a tractor driver. Depending on the yield, the overall usage human labor for harvesting throughout the season ranges from 5789 to 7236 h/ha and machine work from 13894 to 17367 kWh/ha. Mass productivity per worker ranged in the interval 17.14 to 21.42 kg/h. Processing fruits of cucumber pickles was done by facility that serves 12 employees and a total commitment of human labor and machine work was 165 h/ha and 522 kWh/ha. The total average seasonal amount engaged for harvesting, transport and processing of amounts to 6771 h/ha human labor work and 20639 kWh/ha machine work.

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)0.000
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 score0.010
Threshold uncertainty score0.019

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.001

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 head0.009
GPT teacher head0.184
Teacher spread0.174 · 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

Citations0
Published2016
Admission routes1
Has abstractyes

Explore more

Same venueSavremena poljoprivredna tehnikaSame topicAgricultural Engineering and MechanizationFrench-language works237,207