A New Maritime Archaeological Landscape Formation Model: Thunder Bay National Marine Sanctuary
Bibliographic record
Abstract
Archaeology should strive to explore and seek to improve our understanding of human behavior. Underwater archaeology, especially shipwreck archaeology, tends to be particularistic focusing on the human activities associated with a ship or shipwreck itself. Human behavior and its resultant material remains exist on a physical and cultural landscape and cannot be separated from it. Studying known archaeological sites within the landscape reveals patterns of human behavior that can only be identified within that context. This research explores the relationship between the social and natural world and the archaeological landscape at Thunder Bay National Marine Sanctuary and Underwater Preserve. The 448 square miles of sanctuary range in depth from a few feet to nearly 200 feet, and hold at least 100 known and identified shipwrecks and perhaps another 100 unidentified shipwrecks, at various depths ranging from zero to over 100 feet. The lake floor is also littered with ship timbers, wrecked cargo and hardware, fishing gear, and other cultural debris. The natural environment constrains and informs human behavior and plays a large and important role in the development of maritime culture and the maritime landscape. The processes by which this occurs can also be studied through analysis of the archaeological record. The focus of this research is an approach to integrating the components of the maritime landscape with the understanding of the archaeological and historic records as well as oceanographic processes in the Great Lakes to develop a new phenomenological model that takes into account not only the shipwrecks but also the totality of the remains of human activity in a region both on land and on the water. Three levels of analysis associated with the model are: that a vessel will wreck or become irrecoverable in a given location; that wreck material will arrive at a given location; and that wreckage material will survive at a given location. Three general goals are associated with the application of the model: to determine the importance of each behavioral and natural input to each level; to determine the importance of each level in determining the location where archaeological materials may be identified; and to determine if it is possible to derive the agent human activity from the total collection of archaeological material that led to its initial deposition and in many cases modification. This in turn facilitates the determination of higher-order broad anthropological questions to ask of the archaeological record. The efficacy
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".