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
abnormal amplitude suppression 156, 159 AcoustiSens fiber 207 air pressure barometric pressure 36 diffusion 37 experimental methods analysis 43-47 Barrier experiment 42 Bourns BPS110 Series 37 Drained experiment 39-43 Dry experiment 38-39 Fill-to-29 experiment 42 Fill-to-42 experiment 39-41 48 experiment 42 LabJack 38 measurement 38 sand-packed column 37 saturated zone 48-50 vadose zone 47, 48 "Alpha" sensor 61 amplitudes 84-86, 85 amplitude spectra 491, 491 Anadarko Basin 514-517, 516 anelastic mechanisms 258 ANN.see artificial neural network anomalous amplitude attenuation (AAA) 184 aquifer thermal energy storage (ATES) systems 387 artificial neural network (ANN) 523 attenuation estimation, methodology for DAS data 271 laboratory measurement acoustic measurements 266-267 poro-perma measurement 266 resonance intensity spectrum 267 sample preparation 266 saturation 266 scattering and intrinsic attenuation 271-273 sonic data 267-268 waveforms 267, 268, 269 surface seismic data 268-271 VSP data 268 MMFS method 268, 269, 270 axial strain to velocity 281-283 Bajiaoting structure 137 Bakken 508-511, 509, 510 ball-activated cemented single point entry sleeves (Ba-cSPES) 514 Barrier experiment 42 beamforming methods 90-91 Bedretto Valley 320, 321 "Beta" sensor 61 Biot-Willis coefficient 44 bond index log (BI) 486 borehole DAS acquisition and processing 580-582 borehole DAS ambient noise 479, 480 borehole DAS imaging algorithm 582-583 borehole DAS instrumentation and modeling 579-580, 584-586 borehole DAS monitoring 583-584 borehole drilling methods 226-227 borehole-driven surface 143, 143 bottom hole assembly (BHA) 316 Brillouin Optical Time Domain Analysis (BOTDA) technique 559 bubble suppression 157, 159 cable-based deployment 133 CADZOW filtering 168 Canadian Dip-in DAS (CanDiD) projects 514, 532-536, 532-
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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.883 |
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