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 OpenAlex

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

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.

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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.422
Threshold uncertainty score0.529

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 head0.009
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