From west to east: Environmental influences on the rate and pathways of Polynesian colonization
Bibliographic record
Abstract
The prime movers behind the prehistoric colonization of Remote Oceania, and in particular the large c. 2000-year temporal gap (i.e. ‘long pause’) seen between West and East Polynesia, has long been major point of interest in the Pacific. To address these events and the processes that may have led to the known chronological disparity of these diasporas, we present results from two different, but equally powerful, analytical tools which are used to examine Polynesian seafaring capabilities and trajectories. The first is a statistical model known as Seascape, which simulates voyages, while the second uses ease of eastward travel estimates based on land distribution and wind pattern analysis. These analyses were done with the goal of determining the potential role of environmental factors in the colonization process, particularly as they relate to the long pause. We show that the eastern boundary of West Polynesia, the limit of the initial colonization pulse, is marked by a discontinuity in land distribution, where the distances travelers would have to cross in order to reach islands further to the east become significantly larger. At the same time, in West Polynesia, the frequency and intensity of winds favorable to eastward displacement decrease continuously from west to east. As far as winds are concerned, eastward travel in West Polynesia is favored in the northern and southern areas and much more difficult across the central portion. Favorable winds have a clear seasonality, and eastward displacement along the northern area is much easier under El Niño conditions. Voyaging simulations show that intentional eastward voyages departing from Tonga and Samoa, when undertaken with vessels capable of sailing efficiently against the wind, afford a viable route toward several island groups in East Polynesia, with trips starting in Samoa having a higher probability of success.
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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 | 0.003 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".