TOPKAPI-ETH Rio Santa model set-up for publication: 'Thin and ephemeral snow shapes melt and runoff dynamics in the Peruvian Andes'
Bibliographic record
Abstract
Introduction This data deposit contains the model files required to run TOPKAPI-ETH for the Rio Santa catchment. The set-up as saved here is suitable for running the model to produce the majority of the outputs associated with the paper 'Thin and ephemeral snow shapes melt and runoff dynamics in the Peruvian Andes'. This model set-up will produce the 'MB_26' outputs in the related dataset of the model outputs. With changes to the RS.TPK file to save only the daily snow grids it could also be used to produce the 'MB_27' model outputs saved in the same dataset. An overview of the model and the input and output data required is provided in the file TManual_Aug2013.pdf, and further details on the parameters and model outputs are provided in TOPKAPI-ETH Documentation.xlsx. Note these documents have not been fully updated, if you have questions please get in touch with Catriona Fyffe. How to run the model Download the files to your directory on your PC or HPC. Adjust filepaths as needed. Note that currently the filepaths are for Linux, so if you are running on Windows then you'll need to adjust the slashes too. File paths need to be updated in the RS.TPK file and in the run files (see step 5). Create outputs and log folders. The output folder should be in the form Outputs/MB_XX, and the filepath is set in RS.TPK under [SimOutput]. If you are running on an HPC system you may also need to create a folder for log files, as set in the TPK_slurm_wine.sh file. Check the model set-up and parameters. These are all adjusted in the RS.TPK file. See TOPKAPI-ETH Documentation.xlsx, TPK tab for help. Run the model. This can be done either from MATLAB using the file TPK_run.m (please adjust filepaths, then run the file) or using an HPC system in Linux, in this case TOPKAPI-ETH can be run in the Windows emulator Wine. An example Slurm script is available called TPK_slurm_wine.sh. Please adjust the filepaths and requirements as necessary for your system. You will then need to submit the job to your HPC system. Running TOPKAPI-ETH for your own catchment If you'd like to run TOPAKPI-ETH for your catchment then we highly recommend that you use the new version Topwatch. It is currently under development by the team at the Institute of Environmental Engineering, ETH Zurich. We would therefore ask you to get in touch with Prof. Peter Molnar for further information.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.057 | 0.020 |
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".