Analysis of Seasonal Behavior, Cycle Occurrences and Price Trends of Brazil nut Products Exported from Brazil
Bibliographic record
Abstract
The exploitation of non-timber forest products (NTFPs) represent a way of subsistence for Amazon extractive communities, which demonstrate great recognition of its importance to income generation, notably in rural areas. This paper aims to identify and analyze the existence of seasonal behavior, cycle occurrence and price tendency upon Brazil nuts products (Bertholletia excelsa H.B.K.) exported by Brazil to international market during 2005 to 2015. Products quantity and price database were collected from Foreign Trade Information Analysis System (AliceWeb) and used as proxy to estimate its unit price in US$/kg. Deflated by the Consumer Price Index (CPI), using as reference base December 2015, the analysis consisted on applying the Mobile Geometric Mean (MGM) and the ARIMA econometric models. The evaluation of cycles and tendency were realized by graphic analysis of the stationary indexes, visual identification of structural series breaks and plotting reference value to analyze the occurrence of increase or decrease price. Because of the models application a seasonal price behavior was observed for both Brazil nuts products analyzed, shelled and in shell. Although the tendency of price growth was verified for both, the in shell products presented short term annual cycles, while for the shelled product only three long term cycles with distinct intervals were noticed.
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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".