A global hybrid tropical cyclone risk model based upon statistical and coupled climate models - Supporting figures and data
Bibliographic record
Abstract
Introduction This contribution consists of 1) supporting figures and 2) supporting data for the submitted manuscript "A Global Hybrid Tropical Cyclone Risk Model based upon Statistical and Coupled Climate Models." Supporting figures are presented in two interactive HTML documents. The supporting datasets contain tropical cyclone event sets, catalogs, and an example analysis that plots summaries of the simulated catalogs and compares them to historical observations. All files are provided for the 400 ensemble members (event sets) that represent climate model years 1981-2020 (40 years) from the first 10 CESM-LE members. Contents ./Catalog/sim_650 Tropical cyclone annual catalog for each basin based on the CESM-LE distribution of ENSO phases. 650 simulations are provided for each of 400 event sets. Each file contains the catalog for a single basin and is written as catalog_tc_(BASIN)_sim650_my400_nbinom_condmeanensojma.csv., where BASIN can be NA, EP, WP, NI, SI, SP. ./Documentation TCMODEL_UQAM_EXAMPLE.html: Analysis script showing example of use of the tropical cyclone catalogs and comparison to historical observations. UQAM_TC_Model_Data_Supplement_Dictionary.xlsx: Data dictionary of all data supplement file contents. ./IBTRACS Summary of IBTrACS data required in the analysis script. ./TrajectoryBanks Contains subdirectories for each basin (EP, NAT, NI, SI, SP, WP) Each subdirectory contains several files summarizing the event sets, or banks, of tropical cyclone trajectories. Versions Version 1.0.1: Updated TCMODEL_UQAM_SUPPORTING_FIGURES.zip for revisions to submitted manuscript. Version 1.0.0: Original version.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.027 | 0.003 |
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".