Microhabitat Variability in Human Evolution
Bibliographic record
Abstract
Climate variability and hominin evolution are inextricably linked. Yet, hypotheses examining the impact of large-scale climate shifts on hominin landscape ecology are often constrained by proxy data coming from off-site lake and ocean cores and temporal offsets between paleoenvironmental and archaeological records. Additionally, landscape response data (most commonly, records of vegetation change), are often used as a climate proxy. This is problematic as it assumes that vegetation change signifies global or regional climate shifts without accounting for the known non-linear behavior of ecological systems and the often-significant spatial heterogeneity in habitat structure and response. The exploitation of diverse, rapidly changing habitats byHomoby at least two million years ago highlights that the ability to adapt to landscapes in flux had emerged by the time of our genus’ African origin. To understand ecosystem response to climate variability, and hominin adaptations to environmental complexity and ecological diversity, we need cross-disciplinary datasets in direct association with stratified archaeological and fossil assemblages at a variety of temporal and spatial scales. In this article, we propose a microhabitat variability framework for understandingHomo’s adaptability to fluctuating climates, environments, and resource bases. We argue that the exploitation of microhabitats, or unique ecologically and geographically defined areas within larger habitats and ecoregions, was a key skill that allowedHomoto adapt to multiple climates zones and ecoregions within and beyond Africa throughout the Pleistocene.
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 | 0.000 | 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 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".