Progression and Issues in the Mesoamerican Geospatial Revolution
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it