A process‐depositional model for the evaluation of archaeological potential and survey methods in a boreal forest setting, Northeastern Alberta, Canada
Bibliographic record
Abstract
Abstract More than 1,000 archaeological sites occur within the Clearwater‐Athabasca Spillway, a relict channel that routed catastrophic drainage from glacial Lake Agassiz during deglaciation of northeastern Alberta. This high site density is rare in the region, and artifact assemblages are large due to the presence of abundant sources of lithic raw material. Unfortunately, sites are rarely preserved in stratified or deeply buried deposits. As is often the case in subarctic areas, this lack of depositional context coupled with a paucity of datable organic materials has hindered the establishment of cultural chronologies for the region. To address this issue, we develop a process‐depositional model and digital terrain analysis to identify where thicker sediments may have accumulated, and assess whether survey strategies have adequately tested these areas. We find current survey strategies are biased to testing upland ridges with thin deposits, and that inconsistent methods of recording sediment thickness make it difficult to assess whether vertical profiles are being sampled to sterile deposits. We recommend that future survey strategies in boreal forest settings focus on a broader suite of landforms and landform elements, including those that act as sediment traps.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 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 | 0.000 |
| 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".