Nomad-FAIR North-Remote-Tool Example Dataset Orientation Microscopy
Bibliographic record
Abstract
apm_sprint14_apav_usa_denton_smith.zip sha256sum R5038_00333-v02.epos e17531c6dd1528016e2144e9c4b649f0ab385f26cffab5bac3ea3b0386a9ac67 sha256sum rng_5pj.rrng 69833cfd4b7b76a54f08055393a4e264afed14b2de4c0bc8f66fd0bccf6cafb1 (These datasets were shared by the authors of the corresponding paper to the APAV package as is detailed here: https://github.com/openjournals/joss-reviews/issues/4862 and here https://joss.theoj.org/papers/10.21105/joss.04862 The *.epos file is a typically dataset from a GBCO material, sort of which are described in the following publication: https://doi.org/10.1017/S1431927621012794 the APAV documentation mentions that this dataset can be used for exploring e.g. multihit capabilities of APAV. Enabling users to explore APAV especially coupled to the apmtools container in the NOMAD Oasis / NOMAD Remote Tools Hub is the main motivation to have the dataset curated here.) em_om_sprint14_01.zip sha256sum Forsterite.ctf.mtex c028333173d8d9d9094c8121d854ffbb638d94746612743f262a586ee07a61d8 Forsterite.ctf.mtex (This is example was generated from the classical Matlab/MTex texture toolbox Forsterite dataset using an MTex script) sha256sum H5OINA_examples_Specimen_1_Map_EDS_+_EBSD_Map_Data_2.h5oina 163ce6ae95c373727785287ddcdce30c71b83e717ef8914c5212d8f8a2248c93 (This is an example dataset that was shared by P. Pinard to support the development of EDX/EBSD parsing for NOMAD) sha256sum PrcShanghaiShi.EBSPs70deg.zip d614d2db2e54e03ec9cb2eede7e6f66d029dc82ad64cc099d3c7fb1b2be3c5ec (This is a very small subset of the here published dataset https://zenodo.org/record/7528088#.ZFFybnZBy38) em_om_sprint14_02.zip sha256sum SmallIN100_Final.dream3d aea101e5e4cc5f67613a1d041c1979e5bba60c6c6ec305718b2afdf96d47d14b (This is dataset was processed with the SmallIN100 example included and referred to in 5DREAM3D-6.5.163-Win64)
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.224 | 0.318 |
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".