Bibliographic record
Abstract
Wet landscapes have figured significantly in the development of human societies and in the lives of many people through the ages. The water-saturated, low-oxygen conditions in these sites help preserve wood and other plant remains for thousands of years, thus saving fragile material evidence that would otherwise be absent from the archaeological record. Hidden Dimensions is a collection of essays drawn from papers presented at an international conference in Vancouver, British Columbia in April 1995. Scholars from around the globe examine several aspects of wetland archaeology in North America, Mexico, Europe, eastern Siberia, and New Zealand. Some of the essays in this volume explore environmental and historical contexts of wet-sites as well as past human adaptation to wetland environments. Others concentrate on the contributions of wetland archaeology to reconstructions of cultural history and the interpretation of unique perishable materials. In addition to discussions on the dynamic nature of wetlands and concern about the future of the cultural resources they contain, the authors look at practical issues of land management and object conservation. In Hidden Dimensions the authors seek to raise awareness of the significance of wetland archaeology issues at a time when wetlands around the globe are rapidly shrinking and their cultural contents are at risk of disappearing.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.009 |
| Scholarly communication | 0.010 | 0.013 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.045 | 0.012 |
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".