Nonlinear characteristics of the surface air temperature over Canada
Bibliographic record
Abstract
Nonlinear characteristics of the Canadian surface air temperature (SAT) were investigated by applying a neural‐network‐based nonlinear principal component analysis (NLPCA) method to the SAT anomaly data for individual seasons. The SAT data were separated into three subsets: data for 1900–1949 and 1900–1995 over southern Canada (south of 60°N), called S0049 and S0095, respectively, and data for 1950–1995 over the entire country, called C5095. The NLPCA was computed for the three data sets separately. The leading NLPCA modes from C5095 and S0095 show similar results: the nonlinearity is strong in winter (December, January, and February, DJF) and fall (September, October, and November, SON), but is much weaker in spring (March, April and May, MAM) and summer (June, July, and August, JJA), manifesting the seasonal dependence of the nonlinearity in the Canadian SAT. No significant nonlinearity is detected from data set S0049, even for the winter and fall seasons, indicating interdecadal dependence of the nonlinearity. The leading NLPCA mode combines the effects of Pacific‐North America (PNA) pattern and North Atlantic Oscillation (NAO) on the Canadian winter SAT. A possible reason for the existence of nonlinearity in the winter SAT only after 1950 is that the NAO manifested its strong negative phase from the 1950s to the early 1970s.
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 | 0.000 | 0.001 |
| 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 | 0.000 |
| 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".