Detecting British Columbia Coastal Rainfall Patterns by Clustering\n Gaussian Processes
Bibliographic record
Abstract
Functional data analysis is a statistical framework where data are assumed to\nfollow some functional form. This method of analysis is commonly applied to\ntime series data, where time, measured continuously or in discrete intervals,\nserves as the location for a function's value. Gaussian processes are a\ngeneralization of the multivariate normal distribution to function space and,\nin this paper, they are used to shed light on coastal rainfall patterns in\nBritish Columbia (BC). Specifically, this work addressed the question over how\none should carry out an exploratory cluster analysis for the BC, or any\nsimilar, coastal rainfall data. An approach is developed for clustering\nmultiple processes observed on a comparable interval, based on how similar\ntheir underlying covariance kernel is. This approach provides interesting\ninsights into the BC data, and these insights can be framed in terms of El\nNi\\~{n}o and La Ni\\~{n}a; however, the result is not simply one cluster\nrepresenting El Ni\\~{n}o years and another for La Ni\\~{n}a years. From one\nperspective, the results show that clustering annual rainfall can potentially\nbe used to identify extreme weather patterns.\n
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".