Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The ATLAS trigger successfully collected collision data during the first run of the LHC between 2009-2013 at different centre-of-mass energies between 900 GeV and 8 TeV. The trigger system consists of a hardware Level-1 and a software-based high level trigger (HLT) that reduces the event rate from the design bunch-crossing rate of 40 MHz to an average recording rate of a few hundred Hz. In Run-2, the LHC will operate at centre-of-mass energies of 13 and 14 TeV and higher luminosity, resulting in roughly five times higher trigger rates. A brief review of the ATLAS trigger system upgrades that were implemented between Run-1 and Run-2, allowing to cope with the increased trigger rates while maintaining or even improving the efficiency to select physics processes of interest, will be given. This includes changes to the Level-1 calorimeter and muon trigger systems, the introduction of a new Level-1 topological trigger module and the merging of the previously two-level HLT system into a single event filter farm. A few examples will be shown, such as the impressive performance improvements in the HLT trigger algorithms used to identify leptons, hadrons and global event quantities like missing transverse energy. Finally, the status of the commissioning of the trigger system and its performance during the 2015 run will be presented.
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.
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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 it