Human adaptation to Holocene environments: Perspectives and promise from China
Bibliographic record
Abstract
This paper reviews recent archaeological research on human-environment interaction in the Holocene, taking continental China as its geographic focus. As China is large, geographically diverse, and exceptionally archaeologically and historically well-documented, research here provides critical insight into the functioning of social-natural systems. Based on a broad review of the field as well as recent advances and discoveries, the authors reflect on research themes including climate change and adaptive systems theory, spatial and temporal scale, anthropogenic environmental change, risk management and resilience, and integration of subdisciplines. These converge on three overarching conclusions. First, datasets relevant to climate change and ancient human-environment interaction must be as local and specific as possible, as the timing of environmental change differs locally, and the human response is highly dependent on local social and technological conditions. Second, the field still needs more robust theoretical frameworks for analyzing complex social-natural systems, and especially for integrating data on multiple scales. Third, for this work to contribute meaningfully to contemporary climate change research, effective communication of research findings to the public and to scientists in other disciplines should be incorporated into publication plans.
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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 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".