Bibliographic record
Abstract
Sumenep is one of the largest agricultural commodity producing areas in Madura, especially food commodities. So that the Agriculture, Forestry, and Fisheries Business Fields become one of the idols in the economy in the Regency. The purpose of the study is to determine the state of the position of the agricultural sector in the economy in the period 2017 to 2019 in the Sumenep Regency area. 2) Knowing the description of the field in Sumenep Regency in the future. The method used in this research is quantitative descriptive analysis with Location Quotient data analysis, and Dynamic Location Quotient. The results of the study show: 1) The existing economic sources in Sumenep Regency in 2015-2019 are agriculture, electricity and gas sector, mining and excavation sector, clean water, business sector, restaurant and hotel, finance, company services and rental. The sub-sector as the basic sub-sector for the economy in Sumenep Regency during 2015-2019 consists of the plantation crop agriculture sub-sector, plantation agriculture sub-sector, food agriculture and search. 2) The economic sector of Sumenep Regency during 2015-2019 which experienced a change in position in the future there were 15 sectors and which did not experience a change in position and remained in the previous position there were two sectors. The sector changed from the basic sector to non-base. The sub-sectors of Sumenep Regency where there will be a change in position in the future during 2015-2019 are agricultural crops, plantation crops and agricultural services from base to non-basic, while horticultural crops and livestock from non-basic.
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 imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".