Does the Locally-Adaptive Model of Archaeological Potential (LAMAP) work for hunter-gatherer sites? A test using data from the Tanana Valley, Alaska
Bibliographic record
Abstract
We report an assessment of the ability of the Locally-Adaptive Model of Archaeological Potential (LAMAP) to estimate archaeological potential in relation to hunter-gatherer sites. The sample comprised 182 known sites in the Tanana Valley, Alaska, which was occupied solely by hunter-gatherers for about 14,500 years. To estimate archaeological potential, we employed physiographic variables such as elevation and slope, rather than variables that are known to vary on short time scales, like vegetation cover. Two tests of LAMAP were carried out. In the first, we used the location of a random selection of 90 sites from all time periods to create a LAMAP model. We then evaluated the model with the remaining 92 sites. In the second test, we built a LAMAP model from 12 sites that pre-date 10,000 cal BP. This model was then tested with sites that post-date 10,000 cal BP. In both analyses, areas predicted to have higher archaeological potential contained higher frequencies of validation sites. The performance of LAMAP in the two tests was comparable to its performance in previous tests using archaeological sites occupied by agricultural societies. Thus, the study extends the use of LAMAP to the task of estimating archaeological potential of landscapes in relation to hunter-gatherer sites.
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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 | 0.024 | 0.047 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 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".