Trends in temperature and precipitation in the Pacific Northwest during the twentieth century
Bibliographic record
Abstract
Documenting long-term trends or persistent shifts in temperature and precipitation is important for understanding present and future changes in flora and fauna. Carefully adjusted datasets for climate records in the USA and Canada are combined and used here to describe the spatial and seasonal variation in trends in the maritime, central, and Rocky Mountain climatic zones of the Pacific Northwest. Trends during the 20th century in annually averaged temperature (0.7degreeC-0.9degreeC) and precipitation (13%-38%) exceed the global averages. Largest warming rates occurred in the maritime zone and in winter and at lower elevations in all zones, and smallest warming rates occurred in autumn and in the Rockies. Largest increases in precipitation (upwards of 60% per century) were observed in the dry areas in northeast Washington and south central British Columbia. Increases in precipitation were largest in spring, but were also large in summer in the central and Rocky Mountain climatic zones. These trends have already had profound impacts on streamflow and on certain plant species in the region (Cayan et al. 2001), and other important impacts remain to be discovered. The warming observed in winter and spring can be attributed partially to climatic variations over the Pacific Ocean, and the buildup of greenhouse gases probably also plays an important role
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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 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".