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 .The advance of deforestation reveals a small part of the socio-environmental problem related to the Amazonian forests.A recent study 5 quantified that fire, forest fragmentation and logging between 2001 and 2018 have already impacted more than 5.5% of the forests in the entire Amazon basin, and this extension corresponds to 112% of the total area deforested in that period.If we add to this list of forest degradation vectors the occurrence of extreme droughts, the area increases to 38% of the remaining Amazonian forests.It is widely known that fire is the main instrument for disposing of biomass after clear-cutting the forest, and that it causes a series of negative socioeconomic and environmental impacts.For example, on the global scale, greenhouse gas emissions from slash-and-burn practices and wildfires directly affect the rainfall and temperature regime and, on the regional scale, directly generate air pollution, thus affecting air quality 6,7 .Locally, the negative impacts of fires include the degradation of soils and forests, the imposition of restrictions and losses on those who depend on them, affecting their properties, public infrastructure or even services 8 .However, it is much less known that areas with forest fragmentation 9 , logging 10 , and forest areas that have already been affected by fire are more susceptible to new fires 11 .Moreover, extreme droughts, which have intensified and become more recurrent due to climate change 12 , amplify the extent and magnitude of fires 7 .This means that even if deforestation rates are controlled, there are still all the other forcings that lead to the occurrence of fires present in human practices and Amazonian landscapes 13 , and these have increased over this century 14 .
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.126 |
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; both teacher heads agree on what is shown here.
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".