Bibliographic record
Abstract
Lake deposits act as natural archives of past environmental changes. The study of lake sediments, or paleolimnology, can complement archaeological investigations because the environmental consequences of prior human activities (e.g., agriculture, metallurgy, land clearance) are preserved in lacustrine deposits. Moreover, paleolimnology can be used to reconstruct past climatic conditions under which previous cultures flourished or declined, providing an ecological context to help interpret sociopolitical change. Typically, archaeologists have used paleolimnological approaches to supplement their studies by: (1) tracking the impacts of ancient cultures on the local environment (e.g., eutrophication, erosion); (2) determining the activities and cultural practices of past civilizations (e.g., agriculture, mining); (3) directly tracking the presence/absence of humans using unequivocal indicators such as tiny artifacts (e.g., microdebitage) or distinct biomarkers (e.g., fecal steroids); and (4) obtaining a holistic reconstruction of climate and catchment‐related changes, as well as food availability and water quality/quantity, providing an environmental context to the growth and decline of past cultures.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.126 | 0.043 |
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".