Bibliographic record
Abstract
field traps see Traps AC Stark effect 82, 84, 90, 96, 97-101, 104-109 Adiabatic approximation 3, 10, 32 potential energy curves 6-8 Alternating gradient deceleration 155 trapping 160-161 Angle-resolved scattering 198-200, 271-272 Arthurs-Dalgarno representation 211-213, 234-237 Atom-molecule collisions 221-234 Avoided crossing 176, 178, 181, 269, 271 b Background scattering 246, 250 Barrierless reactions see Insertion chemical reactions Beams see also Molecular beams focused 146-151 Gaussian laser 166 guided 149, 151 slow 151-155 Bessel functions 192, 193, 206, 208 Bethe threshold scattering 214 Blue shift 96 Body-fixed coordinate frame see Molecule-fixed coordinate frame Boltzmann averaging, 214, 284 Born-Oppenheimer approximation 1, 32 Bose-Einstein Condensation 262, 293 Bosons 115, 200, 201, 284 Bound state calculations 253-255 Bound states laser-field aligned 134 quantum pendulum 127, 129-131 resonant 241, 246, 252 Breit-Wigner equation 252, 253 Buffer gas cooling 164 c Canonically conjugate variables 113-114 Center-of-mass separation 12, 32, 75, 79, 189, 200, 260
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.771 | 0.698 |
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".