DETECTION OF MAYA <i>SACBEOB</i> (SACBES) USING OPTICAL AND SAR IMAGERY IN NORTHERN PETÉN, MEXICO
Bibliographic record
Abstract
Abstract. The pre-Contact Maya peoples build roads (Sacbeob) to facilitate movement between and within cities and surroundings. Given the dense forest cover in the Lowland Maya region now known as Yucatan, ground surveys of sacbeob are time-consuming and challenging to perform. Remote sensing can be a good alternative as it offers the advantages of extensive regional coverage, zero disturbances to cultural resources, and an opportunity to acquire data in less accessible areas on a cost-effective basis. LiDAR technology is highly valuable to detect man-made structures, but this technology is costly and is time-consuming to acquire data over a large area. Satellite imagery presents an alternative for mapping large areas. Previous studies documented linear features that could represent sections of sacbeob between Calakmul and El Mirador using Landsat-5 TM green, red, and near-infrared images (Folan et al. 1995). This study used Landsat-7 and Landsat-8 optical images having more bands and radar imagery (Sentinel-1 C-VH&VV, and Alos-1 PalSAR L-HH&HV) to connect sacbeob mapped by Folan et al. (1995) between three ancient Maya cities (Calakmul, El Mirador, and Uxul). Our results suggest that radar images with the capacity to penetrate dense forest cover can contribute to the identification and documentation of ancient road systems. Such a study provides an ideal starting point for targeted ground verification.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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 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".