Agriculture in the Ancient Maya Lowlands (Part 1): Paleoethnobotanical Residues and New Perspectives on Plant Management
Bibliographic record
Abstract
Abstract We focus on pre-Columbian agricultural regimes in the Maya Lowlands, using new datasets of archaeological wood charcoal, seeds, phytoliths, and starch grains; biological properties of plants; and contemporary Indigenous practices. We address inherited models of agriculture in the lowlands: the limitations of the environment (finding more affordances than anticipated by earlier models); the homogeneity of agricultural strategies (finding more heterogeneity of strategies across the lowlands than a single rigid template); the centrality of maize in agriculture (finding more reliance on root crops and tree crops than historically documented); the focus on the milpa system as food base (finding more agroforestry, homegardening, horticulture, and wild resource management than previously documented); the dominance of swidden strategies in agricultural practices (finding more diverse practices than accounted for in most models); and the foregrounding of maize crop failure in collapse models (finding more evidence of resilience and sustainable agricultural practices than predicted).
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".