Bibliographic record
Abstract
Time-lapse experiments were performed on the nrms repeatability (NRMS), predictability (PRED) and signal to distortion ratio (SDR) repeatability metrics, and the results studied in order to better understand their meaning. First, controlled time-shift, amplitude and additive noise perturbations were made to a baseline seismic trace. Timeshift had approximately linear effects on NRMS of about 15%/ms, subtle hyperbolic effects on PRED and a negligible effect on SDR. Amplitude tests showed that multiplication of the baseline trace by 0.9 resulted in an NRMS value of 10.5% and SDR value of 10 2.04 Second, all three metrics were calculated using a 2D walkaway vertical seismic profile (VSP) dataset from Violet Grove, Alberta, which consisted of three lines. For Lines 1, 2 and 3, NRMS values were 60.6%, 61.4% and 45.2% for horizontal components, and 46.3%, 42.6% and 41.4% for the vertical component. PRED was 0.73, 0.72 and 0.83 for the horizontal components, and 0.82, 0.83 and 0.87 for the vertical component. Finally, SDR was 10 , while PRED remained unaffected by any amplitude change; analytic equations were found to relate amplitude changes to these metrics. Additive noise experiments revealed that NRMS and PRED are very sensitive to the strength and character of the noise, while SDR seems to be affected little by the noise character.
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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 | 0.004 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".