A comparison of drought information in early North American colonial documentary records and a high-resolution tree-ring-based reconstruction
Bibliographic record
Abstract
Historical documentary records contain valuable information on climate, weather, and their societal impacts during the pre-instrumental period, but it may be difficult to assess the objectivity and reliability of this information, particularly where the documentary record is incomplete or the reliability of the information it contains is uncertain. This article presents a comprehensive review of information relating to drought found in original written records concerning all early European expeditions (1510–1610 CE) into the present-day US and Canada, and compares this information with maps and time series of drought generated from the tree-ring-based North American Drought Atlas (NADA). The two sources mostly agree in the timing and location of droughts. This correspondence suggests that much of the information in these early colonial historical records is probably objective and reliable, and that tree-ring-based drought atlases can provide information relevant to local and regional human historical events, at least in locations where their reconstruction skill is particularly high. This review of drought information from written sources and tree-ring-based reconstructions also highlights the extraordinary challenges faced by early European explorers and colonists in North America due to climatic variability in an already unfamiliar and challenging environment.
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.007 |
| 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) | 0.001 | 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".