Bibliographic record
Abstract
Abstract The Earth sciences have played a key role in understanding the origins and subsequent development of the first American cultures (“Paleoindians”), a broad and often contentious topic in archaeology. Geoarchaeological approaches used to understand the peopling of North America are equally broad at a range of spatial scales. At subcontinental scales, geoarchaeological research revolves around lowered seas levels and fluctuating glacier margins. Modeling sea‐level changes and the high‐precision dating of ice retreat over Canada is helping to understand the environmental conditions, route(s), and timing of the earliest human entry into and colonization of North America. Stratigraphy, a fundamental principle in both archaeology and geology, first established the antiquity and chronology of the earliest artifact assemblages by demonstrating clear association of artifacts and Pleistocene fauna. Many Paleoindian sites also yielded stratigraphic records with evidence of markedly different depositional environments in the past. The ancient fauna and the striking contrasts between past and present depositional environments has long attracted the attention of archaeologists and Earth scientists alike because of the paleoenvironmental implications. Reconstructing the evolution of paleo‐lakes and paleo‐wetlands is aiding in the understanding of ancient landscapes and their evolution. These landscape‐scale studies and micromorphology also provide insights into Paleoindian subsistence.
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 | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".