Sources of Growth and Spatial Concentration of Coconut Crop in the State of Pará, Brazilian Amazon
Bibliographic record
Abstract
The State of Pará contributes to approximately 10.10% of the Brazilian production of coconut (Cocos nucifera L.). It is an important center of production for the crop, mainly due to some factors such as its edaphoclimatic conditions that are favorable for the plant development, the availability of rural credit and the presence of business groups with expertise on the activity and agro industrial processing. This survey used data from Instituto Brasileiro de Geografia e Estatística (IBGE) (2018) to make an analysis of the activity in the state between 1974 and 2016, evaluating by the Shift-Share Analysis the sources of production growth, harvested area and productivity. Furthermore, the study also analyses the evolution of coconut prices, concentration and specialization of some micro regions of the State in coconut crop production using the Locational Gini Coefficient and Location Quotient. The main results show an expressive increase in coconut production in the state of Pará economy since the 1980s, showing that between 1974 and 2016 the production increased by 9.41% per year, the harvested area 7.88% p.a. and productivity 1.42% p.a. It is also possible to observe an expressive concentration and specialization of the activity in the Micro region of Tomé-Açu, responsible for 57.40% of the state production.
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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 | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".