Temporal and Spatial Structure of Nocturnal Warming Events in a Midlatitude Coastal City
Bibliographic record
Abstract
Abstract Nocturnal warming events (NWEs) are abrupt interruptions in the typical cooling of surface temperatures at night. Using temperature time series from the high-resolution Vancouver Island School-Based Weather Station Network (VWSN) in British Columbia, Canada, we investigate temporal and spatial characteristics of NWEs. In this coastal region, NWEs are more frequently detected in winter than in summer, with a seasonal shift from slowly warming NWEs dominating the winter months to rapidly warming NWEs dominating the summer months. Slow-warming NWEs are of relatively small amplitude and exhibit slow cooling rates after the temperature peaks. In contrast, fast-warming NWEs have a temperature increase of several kelvins with shorter-duration temperature peaks. The median behavior of these distinct NWE classes at individual stations is similar across the entire set of stations. The spatial synchronicity of NWEs across the VWSN (determined by requiring NWEs at station pairs to occur within given time windows) decreases with distance, including substantial variability at nearby stations that reflects local influences. Fast-warming NWEs are observed to occur either simultaneously across a number of stations or in isolation at one station. Spatial synchronicity values are used to construct undirected networks to investigate spatial connectivity structures of NWEs. We find that, independent of individual seasons or NWE classes, the networks are largely unstructured, with no clear spatial connectivity structures related to local topography or direction.
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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 | 0.001 |
| 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".