The role of salmon fishing in the adoption of pottery technology in subarctic Alaska
Bibliographic record
Abstract
Ceramic technology makes an abrupt appearance in the New World Arctic at circa 2800 cal BP. While there is general consensus that the ultimate source of these Alaskan pottery traditions lay in continental NE Asia, the motivations for the adoption of pottery in Alaska have remained unclear. Through organic residue analysis we investigated the function of Norton pottery in Southwest Alaska, and the extent to which its function changed in later periods under the increasing northern influence of Thule culture in the region (from ca. 1000 cal BP). Our results show clear evidence of aquatic resource processing in all pottery vessels. Regional variability due to environmental and ecological differences are apparent in the pottery. The majority of Norton pottery was from inland riverine locations and the function of this early pottery was to process anadromous fish, with only limited evidence of other resources. After 1000 cal BP more sites appear on the coast, and while pottery technology changes dramatically at this time, this is not as clear in pottery function which remains aimed at local abundant aquatic resources. We hypothesize that pottery was adopted into Alaska as part of a riverine adaptation and suggest that targeted human exploitation of large riverine systems may have facilitated its expansion into Southwest Alaska. Furthermore, we suggest that this pattern might extend back into Siberia where Alaskan pottery originates.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".