Bibliographic record
Abstract
With the startup of the High-Luminosity Large Hadron Collider (HL-LHC), the Inner Detector (ID) will be replaced with a fully silicon detector known as the ATLAS Inner TracKer (ITk). This new tracking detector is composed of silicon pixel sensors and silicon strips which will extend the tracking detector range up to a pseudorapidity of 4. The demand for a new tracking detector comes as a result of the particle dense collisions produced by HL-LHC. With the increased particle density and radition levels, the success of the ITk requires that it must tolerate the expected dose of radiation while maintaining efficient tracking. Tests have demonstrated that the ITk Pixel detectors' 3D $50\times50\ \mu m^2$ sensors maintains hit efficiency beyond the expected fluence. Additionally, simulations have demonstrated that the ITk, under an average interaction per bunch crossing of $\text{<}\mu\text{>}=200$, will achieve similar tracking efficiency to that of the ID under an average interaction per bunch crossing of $\text{<}\mu\text{>}=38$.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.124 | 0.119 |
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".