Where the Grass is Greener — Large-Scale Phenological Patterns and Their Explanatory Potential for the Distribution of Paleolithic Hunter-Gatherers in Europe
Bibliographic record
Abstract
Abstract A unique property of the Paleolithic record is the possibility to observe human societies in large areas and over long periods of time. At these large spatial and temporal scales, a number of interesting phenomena can be observed, such as dynamics in the distribution of populations in relation to equally large-scale environmental patterns. In this paper, we focus on phenological patterns of vegetation and discuss their explanatory potential for differences in site densities in different periods and parts of Europe. In particular, we present a case-transferable approach to diachronically estimate the timing of the vegetation period and resulting phenological gradients. We discuss results for two complementary case studies. First, we look at the Aurignacian in Western and Central Europe, a period of dynamic population dispersal in a topographically heterogeneous region. Second, we focus on the Middle and Late Upper Paleolithic in the East European Plain, a period after the arrival of anatomically modern humans in a topographically rather uniform area. We visualize phenological trajectories and boundaries otherwise invisible in the archaeological record with certain explanatory potential for the observed archaeological patterns. Importantly, we do not intend to reconstruct specific plant communities or dispersal routes of animals or humans. Rather, we aim at highlighting gradients which in themselves and on small temporal scales might be comparatively weak, but over the course of millennia may potentially influence the distribution of animal biomass and human populations by biasing the aggregate of at times opposing actions of individuals towards particular directions.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".