Bibliographic record
Abstract
This is EIDORS v3.10, released on 31 December 2019. The eidors-v3.10.zip file contains all code and documentation to run EIDORS on all platforms. EIDORS requires Matlab (>=9.1, R2016b) or GNU Octave (>=4.4). Unless otherwise specified inside the respective files, EIDORS functions are licensed under the GNU GPL, either version 2 or version 3 (at your choice). You can find a copy of the license at: www.gnu.org/licenses/gpl.html. Additionally, you can download the model_library.zip file, which contains precalculated additional finite element models for use with EIDORS. The model_library.zip file is not necessary, EIDORS will recalculate these models if they are not available. To use the EIDORS software, follow these steps: - Download the software eidors-v3.9.1.zip - Unzip the software in a directory such as /path/to/eidors or C:/path/to/eidors - Start Matlab / GNU Octave - type: >>run /path/to/eidors/startup.m (windows >>run C:/path/to/eidors/startup.m) - Try the Tutorials, or execute one of the sample programs in the /path/to/eidors/examples directory (such as compare_2d_algs(1)) For new features and other changes with this release, see the CHANGES file in the eidors directory. For other information on EIDORS, see www.eidors.org For questions and comments on EIDORS, you can join the eidors3d-help mailing list.
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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.362 | 0.388 |
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".