Mapping and Environmental Diagnosis in Native Acai Areas in the Amazon
Bibliographic record
Abstract
For several decades, the Acai orchards (acaizais) have directly influenced the survival of the families in the Amazonian floodplains. In this period, the production of the Acai fruit for local consumption was ceased and became an export item produced in intensive management, resulting in an increase in orchards in the floodplains and the emergence of dryland plantations, no longer representing a typical extractive activity in the Amazon. The objective of this study was to map the classes of use and coverage, and the occurrences of the Acai orchards massifs, as well as to analyze the physical and chemical parameters of five islands in the municipality of Igarapé-Miri, State of Pará, Brazil, where there is a great occurrence of productive Acai orchards. This work evaluated the following islands: Jarimbu, Mamangal, Itaboca, Mutirão, and Buçu, where geolocalized collections were carried out in the areas with the highest occurrence of Acai orchards, both to assist in the classification of images and for soil sampling. August 2019 Planet images were processed using the unsupervised method, where seven classes of cover use were obtained: hydrography, exposed soil, urban, alluvial, lowland, arboreal, and agriculture areas. Therefore, occurrences of productive orchards were identified and correlated to the good attributes of soil fertility in the floodplains under continuous flooding and sedimentation. The correlation confirmed the higher productivity of Acai in the Alluvial and Lowland classes, which predominate in the evaluated area, presenting soils considered fertile with a loam-clay-silty and loam -silty texture, high base saturation (greater than 50%), high organic matter content, and significant presence of potassium and phosphorus.
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How this classification was reachedexpand
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 | 0.000 | 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) | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".