Landscape indicators of Old Tower Road archaeological site (DbJm-6), Thunder Bay District
Bibliographic record
Abstract
This thesis addresses two research objectives. The first investigates landscape factors in the paleo-environment which may have influenced the geographic positioning of an archaeological site near Thunder Bay. The time period under consideration is the Plano, or Late Paleoindian, which spans approximately 6500 to 8500 14C years BP in northwestern Ontario (Julig 1994). Secondly, an assessment is made of whether a computer-generated landscape model is able to accurately portray real-world conditions at the present time, and whether this process can be applied to future research projects. Because archaeological sites are often discovered in shoreline environments around Thunder Bay (Hamilton 1996; Phillips 1988), the question arises of whether shorelines may be a major factor in the siting of Plano camps. Field investigations provide evidence that the Old Tower Road site location could have been influenced by its proximity to an ancient shoreline. Other factors that might have also affected the decisions made for that particular site location may never be known. By studying the environs of the Old Tower Road site in detail, landscape indicators may provide important clues (Fry et al. 2004). Put simply, the query is, "Why is it there?" Weeks of thesis fieldwork permitted a landscape visualization that includes a proglacial lake approximately 2 km north of the study site, one or more debris flows in a high-energy alluvial environment, and the presence of humans who manufactured stone tools at some time period, possibly related to these events. Due to insufficient spatial resolution of the DEM which was created for use in a GIS application, the terrace feature which was discovered during fieldwork is not visible in the final map document. Landscape visual cues may potentially be used in archaeological site prediction (Bellavia 2002; Ebert 2004), although that is not a primary focus of this thesis.
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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 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".