Land use, settlement, and plant diversity in Iron Age Northwest France
Bibliographic record
Abstract
Various studies using pollen stratigraphies have demonstrated significant correlations between Holocene plant diversity, climate, and human activities. Studies that have analyzed longer Holocene timescales tend to discuss cultural data very superficially. This is remarkable because detailed insights into past human activities may be key to gain an understanding of the observed trends in biodiversity. This study aims to reconstruct and explain spatio-temporal trends in past plant diversity (alpha, temporal, and spatial beta diversity) by integrating data on vegetation dynamics, human subsistence economy, and land-use patterns. The landscape of Northwest France during the greater part of the Iron Age and the start of the Roman period (600 BC-AD 100) is selected as a case study. In total, 30 high-quality pollen-stratigraphical sequences allow for the reconstruction of the main long-term trends in plant diversity and more generally of the changing fabric of the landscape. Additionally, increasingly detailed images of the Iron Age rural landscape are available because of a steep increase in archaeological data (aerial photography, surveys, and excavations). These different types of data are integrated and used as input for a wider discussion on the relation between human activities and plant diversity. In general, the taxonomic richness increases steadily during the period under study. Some spatio-temporal differences are observed. The increasing richness values correspond with the growing impact of human activities on the landscape. Archaeologically documented land-use changes on smaller timescales are less clearly reflected in the richness values and vegetation dynamics, which might result from the (large-scale) research design.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".