NetCDF templates and code for creating and loading ATOMIX ADV benchmark datasets
Bibliographic record
Abstract
Summary This database contains tools that help interacting with ATOMIX SCOR working group #160 (ADV subgroup) NetCDF benchmark files. The templates for our recommended metadata was derived from the testing done by working group members with financial support from NSF grant #OCE-2140395 and contributions from national SCOR committees. The ATOMIX wiki has more information about the group's activities. This repository is meant to accompany the ADV's subgroup best practices manuscript, which is about to be submitted. This repo will be updated once the citation is available. Loading NetCDF files Matlab load_netcdf_data('exampleBenchmark.nc') which can be found in this repository in https://github.com/SCOR-ATOMIX/ShearProbes_BenchmarkComparison JuliaLang using NCDatasets NCDataset("exampleBenchmark.nc") GUI tool NASA GISS: Panoply 5 netCDF, HDF and GRIB Data Viewer Yaml templates with example metadata written into benchmark NetCDF file global_tidal_mavs.yml allows the user to write the global metadata for the NetCDF file (instrument model, deployment details, etc) group_metadata.yml shows standard information expected at each processing (group) level within the NetCDF file flags_metadata.yml has metadata associated with variables stored within some of the groups such as EPSI_FLAGS and VEL_FLAGS (e.g., XYZ_VEL_FLAGS, ENU_VEL_FLAGS, or BEAM_VEL_FLAGS). Namely, the thresholds used for the quality-control metrics. These templates are provided to showcase what type of metadata should be written into the NetCDF files. Many programming languages can read yaml, but the user can obviously choose another tool to write the required metadata into their NetCDF files.
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 | 0.004 | 0.016 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.007 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.194 | 0.159 |
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".