Bibliographic record
Abstract
This thesis aimed to produce a more realistic view of the effect of past and current domestication processes on modern Amazonian forests. I tested the hypothesis that Amazonian forests were domesticated to different degrees by past societies and continue to be modified by present-day management practices. The main questions of this thesis are: 1. What are the relative roles of human and environmental factors in shaping the distribution of useful and domesticated plants across Amazonian forests? 2. How do management practices and natural ecological processes interact to form forest patches dominated by useful plants? 3. How do ancient and current effects of human activities vary across forest landscapes? To answer these questions, I investigated the patterns and processes of forest domestication in Amazonia at different spatial and temporal scales (Figure I combined data from floristic inventories, archaeological sites, environmental measures, ethnographic assessments and literature review of useful plants. In some of parts of my thesis (Chapter 2-4), I used existing databases of archaeological sites 3 and floristic inventories collected and organized by other researchers 4 , and basin-wide data on environmental factors that are available online (for more details about these databases see methods section in Chapter 3). I also carried out field surveys along gradients of human influence in different river basins of the Brazilian Amazon (Chapter 5-6). In the field, I interviewed local people, and together with them I carried out participatory mapping and guided tours around their villages. Additionally, I collected soil and plant vouchers of useful species to compare ancient and current land-use histories.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".