An Environment for Searching and Mining Multiple Structural Databases
Bibliographic record
Abstract
The ICSD, published by Fachinformationszentrum Karlsruhe, now contains more than 50,000 structures, including about 10,000 mineral structures.A new retrieval program written by NIST under Windows should be ready at the time of this meeting.My first work for ICSD after my retirement in 1996 was updating the mineral names.About 4,000 were missing, even if the structures themselves were in ICSD.Then about 700 structures with misprints in their publications could be corrected, the most often errors being missing signs, missing leading zeros, interchanged digits, and wrongly doubled digits (for instance: .133instead of .113),an error very difficultly to be detected in proofreading.Further errors were wrong origins of unit cells (most often in P2 1 2 1 2 1 ), wrong space groups, transformed co-ordinates with non-transformed unit cells, and missing angles ß for monoclinic cells.Another problem are the correct constraints for anisotropic displacement factors, especially in trigonal and hexagonal space groups.If possible, cross references to the Powder Diffraction File PDF for new and revised entries are given.During the last two years I added about 5,000 overlooked or forgotten structures published in (entries 40,000 to 45,000).To my experience about 10 % of the structures were missing.The best way for finding forgotten structures would be by assistance of the authors themselves.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.003 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".