A predictive model for archaeological potential for a locality in the Interior Plateau of British Columbia
Bibliographic record
Abstract
Consideration of heritage resources in forestry in British Columbia was mandated by the Forest Practices Code in 1993. Heritage resource planning and management in forestry, however, is often not as easily accomplished as traditional conventional scientific concerns. This is in part due to the nature of the resource. It is considered that less than half of the extant archaeological sites in the Province are known, and the physical presence of many sites are not readily apparent or are difficult to determine without physical examination. This study utilizes normally available biophysical data in digital form to develop a predictive model for heritage potential for a locality in the British Columbia interior. A specific set of environmental criteria are selected, the study site is analyzed for areas satisfying the model criteria, and the appropriate areas are overlaid graphically to produce a predictive map. Finally, the predictive map is coregistered with data of known archaeological sites to evaluate the model. Results from the testing overlay indicate that the area described as having high archaeological potential contains 73% of the currently known sites. These results suggest a positive relationship between the combination of the selected environmental criteria and the location of known sites for the study area. The paper includes research into other predictive models and related archaeological literature, an overview of interior British Columbia pre-history, and reports on consultation with Native Indian persons who live in the area.
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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 | 0.001 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".