Progression and Issues in the Mesoamerican Geospatial Revolution
Bibliographic record
Abstract
Abstract The use of airborne mapping lidar (Light Detection and Ranging), a.k.a airborne laser scanning (ALS), has had a major impact on archaeological research being carried out in Mesoamerica. Since being introduced in 2009, mapping lidar has revolutionized the spatial parameters of Mesoamerican, and especially Maya, archaeology by permitting the recovery of a complete landscape and settlement pattern for further analysis. However, like any new technology, there are learning curves to be overcome, resulting in a feedback relationship between the on-the-ground archaeologists, the virtually grounded computer analysts, and the instrument designers. Archaeologists have been able to identify problems and issues with data production and visualization for the determination of archaeological remains caused by vegetation, special terrain conditions, and modern disturbance. The identification of these concerns helps the technician to develop new techniques, especially when working in conjunction with the field researcher. As seen through the papers in this volume, this symbiotic relationship promises to yield both new breakthroughs in landscape and settlement analysis for Mesoamerican archaeology and enhanced analytic and visualization techniques for lidar with the potential for applicability in other contexts. In many regards, the development of lidar has parallels to the development of radiocarbon dating as a revolutionary technology.
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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 | 0.010 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.007 | 0.015 |
| Scholarly communication | 0.013 | 0.010 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.005 | 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".