Python Scripts for moving information from Celestica spreadsheets to the ATLAS ITk Production Database
Bibliographic record
Abstract
The University of Toronto contributes to the development of the Inner Tracker of ATLAS for the High Luminosity Large Hadron Collider. Celestica, a private company, builds the modules and hybrids. Celestica keeps a log of all the assemblies and tests conducted on the components in a Google spreadsheet. This information must also be uploaded onto the ATLAS Inner Tracker Production Database. The database keeps track of the state and history of all the components. To automate the uploading process, six scripts were written to achieve six different tasks: three for uploading tests and three for assembling components. This report will focus on the assembling scripts. One script assembles hybrid flexes to hybrid assemblies, another assembles hybrid assemblies and powerboards to a module, and the third for hybrid assemblies to hybrid test panels. These scripts require minimal interaction with the user and are built to prevent crashes in case of errors. However, the user must be attentive to any changes or rearrangements made in the spreadsheet. The scripts may need to be modified accordingly to avoid errors. Otherwise, the scripts prove to be an effective method to transfer 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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.291 | 0.201 |
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".