Bibliographic record
Abstract
In ancient Alaska, people allocated wood, bone, and oil for both fuel and non-fuel purposes, which required careful management. By examining these resources through the lens of human behavioral ecology (HBE) and the principle of least effort (PLE), we can understand fuel use—especially woody fuel use—from the standpoint of selectivity, wherein ancient people considered energetic output, handling costs, and state when choosing fuel sources. At any given site, some degree of firewood selectivity, ranging from complete indifference to marked discrimination, would have been most advantageous. Accordingly, ancient Alaskans at Cape Espenberg, Gerstle River, Hungry Fox, and Walakpa would have employed different fuel management strategies tailored according to their evolving needs. Results suggest that firewood indifference was more common, and that selectivity was advantageous only at longer-term occupations where fuel was abundant. Otherwise, proximity and handling costs trumped the benefits of taxon-specific selectivity, which is a strategy meant to confer desired combustion outcomes. Detecting when and where it was beneficial for ancient Alaskans to be selective grants insight into how they categorized fuel and adapted their fuel selection behaviors to fit particular circumstances. Moreover, the restrictions imposed by finite fuel availability have general implications for settlement patterns and mobility that may help trace ancient migration routes as hunter-gatherers leap-frogged from one fuel patch to another.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".