Bibliographic record
Abstract
Abstract Specialists and the general public alike are very aware of human impacts on our environment. Climate change, deforestation, desertification, soil erosion and other topics are currently much in the news, but human influence on the environment is not a new phenomenon. Geoarchaeologists study the traces of human interactions with the geosphere dating back to ancient times, as well as up to and in the present. Geoarchaeological investigations provide the key to recognizing landscape and environmental change within a region, as well as reconstructing ancient landscapes and palaeoclimatic regimes. Such an interdisciplinary approach makes it possible to interpret the ways that humans affect the geosphere, through such things as subsistence and resource exploitation activities, settlement location, and local and regional land-use patterns. This approach also allows us to determine the effects of environmental change on human societies. For millennia, humans have been coping with, or provoking, environmental change. We have exploited, extracted, over-used but also in many cases nurtured the resources that the geosphere offers. In the geoarchaeological perspective, human life has never been separate from nature. Geoarchaeology can thus provide a more inclusive and longer-term view of human–geosphere interactions, and serve as a valuable aid to those who try to determine sustainable policies for the future.
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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 | 0.021 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.008 | 0.055 |
| Scholarly communication | 0.018 | 0.028 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.007 | 0.015 |
| Insufficient payload (model declined to judge) | 0.007 | 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".