The seasonality of precipitation signals embedded within the North American Drought Atlas
Bibliographic record
Abstract
We examine how the seasonality of precipitation signals embedded within the North American Drought Atlas varies across the continent. Instrumental records of average summer (JJA) Palmer Drought Severity Index (PDSI) are characterized by major regional differences in the relative importance of precipitation during summer and winter (DJF). The Atlas, which is based on a network of drought-sensitive tree-ring records, is able to reproduce the main geographic patterns of these biases, but tree-ring reconstructions exaggerate the influence of seasonal precipitation anomalies in the southwestern United States and northern Mexico (towards a stronger winter signal) and western Canada (towards a stronger summer signal). Drought reconstructions from the Southwest and Tex-Mex regions are tuned mainly to winter precipitation and display strong teleconnections to both El Niño and La Niña. In contrast, winter precipitation signals are either weak or absent in drought reconstructions from northwestern North America, and tree-ring estimates of PDSI show a much less robust association with the El Niño-Southern Oscillation. Geographical differences in the relative strength of seasonal precipitation signals are likely due to (i) local factors that influence tree growth but are not incorporated into the PDSI algorithm and (ii) real differences in regional climatology. These seasonal biases must be taken into account when comparing drought reconstructions across North America, when comparing tree-ring PDSI to drought records developed from other proxies or when attempting to use the Drought Atlas to link past droughts to potential forcing mechanisms.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".