Estimativa da população em unidades de conservação na Amazônia Legal brasileira: uma aplicação de grades regulares a partir da Contagem 2007
Bibliographic record
Abstract
In this paper a method for increasing the resolution of census data is tested and presented, by aggregating the data onto a regular grid. The methodology consists of (1) the aggregation of households, represented by their geographical coordinates obtained by the 2007 Population Count, carried out by the Brazilian Census Office (IBGE) and, (2) the unbundling of the data by census tracts on the basis of proportionality. The grids obtained were used to estimate the resident population of 114 conservation units in Brazilian Legal Amazon, all of them instituted in or before 2006. The intention was to test this methodology on territorial units that follow neither the official political-administrative boundaries of states and cities, nor the boundaries designed by IBGE for collecting data. The methodology also contributes to the study of populations living in protected areas, due to the scarcity of population estimates in the conservation units. The results showed a population of 325,398 inhabitants in the selected units, 297,693 of whom were in units for Sustainable Use and 27,705 in Permanent Protection units. Adjoining areas have an estimated joint population of 1,020,237. Despite the limitations involved in using the 2007 Population Count, the aggregating of data into grids would seem to be a promising methodology in view of the improvements in IBGE’s use of geotechnology. The grid minimizes problems that come up in the use of administrative units or census data and may represent an approach that can be applied usefully in demography and other areas of knowledge.
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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".