A GIS-based Case Study of CRM Sites in the Lower Grand River Area, Ontario
Bibliographic record
Abstract
My aim in this study is to identify Late Archaic persistent places—places of continued importance throughout the long-term occupation of a region—within the lower Grand River Area of what is now southern Ontario. I accomplish this through the use of kernel density estimation applied to datasets containing the locations of Late Archaic (4000-2800 RCYBP) sites within this study area which were discovered through cultural resource management (CRM) survey and excavation. Areas identified as persistent places were investigated with regard to landscape features and environmental affordances that could have structured their consistent re-use throughout the Late Archaic, with particular attention paid to the hypothesis that persistent places may have developed around the riverine spawning grounds of spring-spawning fish. Two places with particularly intense concentrations of diagnostic materials dating to successive periods of the Late Archaic were identified: one surrounding Seneca Creek near Caledonia, and one near D’Aubigny Creek south of Brantford. The results show that the persistent use of these places would likely have been structured by the presence of landscape features which would have made these areas particularly rich in many different seasonal resources during the Late Archaic. Perhaps most significantly, both areas are located in close proximity to areas identified as walleye spawning grounds. The contributions of this thesis include the synthesis of the results of many years of CRM survey of the Grand River Area, evidence for the existence of Late Archaic riverine fishing sites related to the spawning runs of walleye, and an improved understanding of Late Archaic subsistence-settlement systems.
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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 | 0.001 | 0.005 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".