Did Native Americans influence the northward migration of plants during the Holocene?
Bibliographic record
Abstract
AbstractLong‐distance plant dispersal explains the rapid northward migration of plant species during the Holocene but the mechanisms by which it occurred are poorly understood. Given that Native Americans spread numerous cultigens over thousands of kilometres during the late Holocene, I examined historical literature for evidence of non‐cultigen dispersal or cultivation in North America's eastern woodlands. Cultivation references are included because a strong relationship between dispersal and indigenous flora husbandry is assumed. Sixty‐seven texts describing Native American lifestyle, cultural activities, and land management reported some form of plant use. Most accounts, however, focus on cultigen production or the use of indigenous flora for medicine or food without mention of dispersal. Twenty‐four of the texts described the trade, transport, or cultivation of plants indigenous to eastern North American woodlands. Most accounts focus on the informal production of food plants, especially trees and shrubs. Confounding these reports was clear evidence of observer bias, limited botanical knowledge, acculturation, and secrecy by Native American informants. Because of these shortcomings, the likelihood of widespread long‐distance plant dispersal by Native Americans could not be determined using historical literature. This activity was either not widespread or was not observed by, or revealed to, Europeans. To adequately test the Native American plant dispersal hypothesis, direct evidence from other sources (e.g. archaeobotancial data) will be required.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".