CoSMoS v2.0: Making Time Series Generation Simple
Bibliographic record
Abstract
Many physically based models aiming to quantify the vulnerability and risk of hydrologic and geomorphic hazards need as input or forcing time series of processes such as precipitation, temperature, humidity, etc. The reliability of their output depends on how realistic the inputs are. CoSMoS is a multi-platform software that generates reliable time series from hydroclimatic variables (precipitation, temperature, wind, relative humidity, streamflow, etc.). It is developed in R (version 2.0) as well as in other platforms (Matlab, Mathematica, Excel). It can be used to generate univariate and multivariate time series at any time scale by reproducing the marginal distributions and the linear correlation structures (including intermittency) of the process under investigation. CoSMoS implements a unified stochastic modelling scheme that expands and enhances a generic modelling approach based on the transformation of “parent” Gaussian time series. By design it aims to offer a simple and easy-to-apply solution to the user requesting minimal information, such as the target marginal distribution and the correlation structure. The software is accompanied by a complete users’ manual.
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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.056 | 0.022 |
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".