Bibliographic record
Abstract
Amazon deforestation data are used as a gauge, at the national and international levels, to indicate the current situation of the political management of the control of and combat against this process, which is usually widely disseminated in the media. Due to the weakening of environmental policies in recent years, there was a forecast that deforestation for the year 2020 1 would be the highest of the decade, above that of 2019, which exceeded 10,800km 2 1 , the highest rate since 2008. Although 2020 had a slightly lower rate than 2019, deforestation in 2021 and 2022 exceeded 12,000km 2 2 , which again featured prominently in global media. Recently, the Yanomami crisis revealed another growing threat to Amazonian life: the push of mining activities in the region. Estimates point to increased mining rates mainly after 2010, and 2020 data showed that the total mining area exceeded the industrial mining area 3 . The negative impacts -beyond social and cultural ruptures caused to indigenous peoplesinclude increased disease rates, environmental contamination, and food insecurity 4 .
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.104 | 0.040 |
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".