What Driving Gross Domestic Product of Agriculture? Lessons from Indonesia (2014-2021)
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.
Bibliographic record
Abstract
Developing markets such as Indonesia are now concentrating open agriculture, which is actualized in the share of global agro-industry.At the same time, the existence of dependence on agricultural products between one nation and another, is articulated as an opportunity and a competitive advantage.This paper evaluating the factors driving the GDP of agriculture in Indonesia.Data duration is 2014-2021.The construction of the analysis is framed by linear regression.It was found that employment in agriculture, precipitation, arable land, crop production, food production, livestock production, and fertilizer have a simultaneous impact on GDP of agriculture.Then, employment in agriculture, precipitation, food production, livestock production, and fertilizer have a partial impact on the GDP of Agriculture.Unfortunately, arable land and crop production do not have a partial impact on the GDP of Agriculture.Long-term prospects consider dimensions that are not influential to be developed holistically.Another point is also considering the GDP of agriculture in a more competitive exploration.Weaknesses of this scientific paper are highlighted for academic contributions and practical compilations.In the future, limitations on data extraction can be developed.Furthermore, practical policy elaboration as the primary key in agricultural institutions, strengthening farmer innovation, and protecting agricultural land from the threat of increasingly extreme temperature depletion and massive settlement development.
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Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it