Clustering analysis of very large measurement and model datasets on high performance computing platforms
Bibliographic record
Abstract
Hierarchical agglomerative clustering is a useful analysis technique which allows for a level of stability, interpretability and flexibility not available in other similar analysis techniques such as K-means, density-based clustering or positive matrix factorization. Previous studies using hierarchical clustering on atmospheric model output have been limited to small domain sizes (roughly 100x100 grid cells) by the computational expense and memory requirements of the algorithm. Here we present hierarchical clustering analysis on two atmospheric datasets which are much larger than was previously possible. In the first case study, we perform clustering on an entire year’s worth of hourly model simulated concentration and deposition data. The model domain covers the Canadian provinces of Alberta and Saskatchewan and has a size of 538x540, making for 290,520 hourly concentration timeseries. The resulting maps identify regions within the modelling domain within which forecast time-series are similar according to the chosen metric – demonstrating the analysis methodology’s ability to objectively and quantitatively define “airsheds” across a larger domain than has been possible before. The identified airsheds differ depending on species.
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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 | 0.003 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".