Bibliographic record
Abstract
Note: Locators for tables and figures are marked in italics.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
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 | 0.002 |
| 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 | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.556 | 0.419 |
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".