Peering Through The Sands Of Time: A Geophysical Investigation Of Two Buried Ships
Bibliographic record
Abstract
In the Spring of 2001, the wooden ribs of an unknown ship were discovered protruding from the beach in Southampton, Ontario. A ground penetrating radar (GPR) survey was performed over the wreck as part of the site characterization. Near surface scattering was used to infer zones of buried wreckage. Test pits were dug based on locations of GPR anomalies and the remains of a sizeable ship and a small barge were partially unearthed. A subsequent magnetic survey was performed to delineate ferrous structural artifacts. The comparison of the geophysical data and excavation results demonstrates the ability of each method to detect and delineate buried structures. Direct interpretation of the GPR profile data highlights the general area of known wreckage, but an exact delineation is not possible. In the case of the magnetic survey, major structural features of the vessels are clearly identifiable and yield information on the size and orientation of each vessel. In October, 2002, a third excavation took place to investigate details of the larger ship. During the course of this excavation, a small deck cannon was discovered among the remains, coincident with a large reverberation event on one of the GPR profiles. This discovery has important historical implications and is considered an extremely significant find in the archaeological context of the Great Lakes.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".