Virtualization of the drenched POOMPUHAR Port utilizing bathymetric data processing
Bibliographic record
Abstract
Marine archaeologists have unearthed Poompuhar, an ancient city, and harbor from the 4th century BCE that was situated in the Mayiladathurai District of Tamil Nadu, a southern Indian state. During the early Chola Kingdom's control, it was a well-established historical and ancient port that emerged with the growing oceangoing trade. This historic city and harbor serve as a metaphor for Tamil Nadu State civilization in the early 20th century. Then, in the year 500 AD, large shore waves swept this city away and destroyed it. These ancient cities and port ruins are still submerged and dispersed around the ocean's deep bottom. In reality, thorough examination and recreation of the life histories of harbour cities like Poompuhar are required to unearth several hidden virtues from the past. High-quality bathymetric data of the ocean floor have been gathered using multi-beam echo sounders. The primary results of the work are the precise identification and geo-tagging of the study region, the processing of the raw bathymetric data, and the thorough removal of undesirable signals. Here we have the interpolation method, which is important in creating bathymetric maps. Inversed Distance Weighting (IDW), one of several interpolation methods that are built into a GIS program, is used to create a bathymetric map based on the input processed bathymetric data. Later, the interpolated surface can be used to create a digital elevation model. The primary topic of this work is IDW, which focuses on a spatial and 3D interpolation approach to map the seabed using the provided input data. As a result, the IDW interpolation technique can be used to detect any submerged terrain anywhere in the world.
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How this classification was reachedexpand
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 | 0.009 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 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 teacher head, 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".