Bibliographic record
Abstract
It was definitely a eureka moment, in fact several eureka moments along the way. The first came in 1991–1992 when I was working in the technology group at Amoco Canada. A key processing step before handing seismic data to the interpreters was shaping the source wavelet in each 2D line to a consistent spectral shape. Some of the data sets contained strong coal reflections overlying the geologic zone of interest. It was very important to stay away from such coal reflections when doing the spectral matching/shaping because the interference from these strong coal reflections would contaminate the wavelet information. Such contaminated spectra seemed to show more about the coals than about the wavelet we were trying to characterize. So the thought was, if a short window around the coals tells us something about the coals, then why don't we see if a short window around a target zone of interest tells us something about that zone of interest. A 2D testline across well control seemed like a good place to start investigating this idea. The resulting trace-by-trace, short window amplitude spectra revealed a great deal of interesting variability. The eureka moment occurred as soon as I saw that those variations in spectral content seemed to be associated with geologic heterogeneity. Repeating the experiment on a 2D stratigraphic seismic model showed interference patterns that were similar to those observed in the real data. At that point, I was hooked and knew there was so much more potential under that rock.
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 | 0.006 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.009 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.008 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".