Bibliographic record
Abstract
The distribution and abundance of plants and animals during the Quaternary is of great interest in the understanding of the pattern for any particular species. In our case it is fundamental to understanding the way in which humans were distributed at different times during the Quaternary. It is important to start our discussion at the macro-ecological scale. The broad biogeographic picture will give us important insights at the scale which is most relevant to our study. We will zoom into lower spatio-temporal scales in later chapters where it is relevant to the discussion. I will not spend time discussing well-established biogeographic patterns that I do not regard to be especially relevant to this book. I am more concerned with the distribution and shifts in distribution of environments that would have influenced human distribution and I will confine my discussion largely to the Eurasian and African land masses which is where the main events took place. Vegetation structure In this book I will place particular emphasis on vegetation structure, that is the three-dimensional arrangement of plants in space. The reason for this is that I consider that vegetation structure will have played a major role in the distribution of humans, as it does for most animals (Bell et al ., 1991). Vegetation structure would have been particularly important in determining the types of potential prey available to humans and also in making prey visible and accessible.
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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 | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.016 | 0.003 |
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".