Data‐fused digital bathymetry and side‐scan sonar as a base for archaeological inventory of submerged landscapes in the Rideau Canal, Ontario, Canada
Bibliographic record
Abstract
Abstract Colonel By Lake, located near Kingston, Ontario, was created in the early 19th century during the construction of the Rideau Canal waterway. Canal flooding inundated a large area of the Cataraqui River lowlands, submerging important pre‐contact and colonial settlements. In order to gain a better understanding of the pre‐canal environment and its archaeological setting, a systematic bathymetry and side‐scan sonar survey was conducted over a 2‐km2 area of Colonel By Lake. A 2‐D digital bathymetric model (DBM) of the lake bottom was constructed and overlain with side‐scan mosaics to map the paleogeography of the river flood plain. The data‐fused sonar images clearly identify the submerged pre‐canal topography, including the former Cataraqui River channel, relict meanders, tree stump fields, and bedrock uplands defining the valley sides. By comparing the DBM with landscapes depicted in pre‐canal period maps (ca. 1828), the locations of several potential archaeological targets were identified. The DBM provides a basis for mapping submerged cultural resources in the Rideau and for predicting the location of undiscovered archaeological sites. The results show that integration of single‐beam bathymetric mapping with side‐scan imagery is an effective strategy for mapping submerged terrestrial landscapes and archaeological inventory in shallow water settings. © 2008 Wiley Periodicals, Inc.
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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 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".