Amazon Expedition Magazine 4th Edition 2025
Bibliographic record
Abstract
Era of Sapiens: The Anthropocene and Artificial Intelligence Have we reached the Anthropocene or not? The group in favor (Anthropocene Working Group – AWG), with research carried out at Lake Crawford (Toronto-Canada), has been collecting data since the 1950s, such as particulate materials in the lake's water profile, and highlights the thesis that the global geoscience scenario clearly reveals changes in environmental geology, highlighting the participation and influence of human beings. However, there is also the opposite group, which argues that we remain in the Holocene. These studies support that climate fluctuations and increases in CO2 levels in the atmosphere are natural characteristics, typical of cycles that occur in previous geological periods, such as the Pleistocene. Humanity goes through three important revolutions throughout its existence. According to Yuval Noah Harari (in Sapiens – A Brief History of Humanity), the first occurred 70 thousand years ago, and is the Cognitive Revolution, the second is 12 thousand years ago, is the Agricultural Revolution; and the third 500 years ago is the Scientific Revolution. Based on this statement, it is possible that the two previous Anthropocene hypotheses are viable, as they fall within this 500-year period. However, most scientific studies are financed with the aim of achieving some political, economic, social or even religious interest. The question arises: “What is more important?” and “What is good for humanity?” So, modern science, coming from the Scientific Revolution, in humans' constant search for something more, evolves into the most elaborate algorithms with Artificial Intelligence (AI). When we talk about AI, we have the idea of a super-powerful information entity; however, this algorithm is composed of several other algorithms, all developed by human beings grouped in a digital environment and available to offer rapid problem solving. The big issue in all this is that other algorithms interfere in the processing of information, giving the impression of “human subjectivity”, aligning the answer to the required question. Currently, it is the Sapiens' greatest success and fear: what if she really learns such “human subjectivity” .In this edition, we will address these topics, in addition to basic research in the Central Amazon and COP-30 (Belém-Brazil). Chief Editor
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.557 | 0.424 |
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".