Bibliographic record
Abstract
Abstract Upon entering the twenty-first century, we see wide-ranging changes in geophysics. As of this writing, quality and utility of geophysical data continues a trend of inexorable improvement punctuated by individual quantum steps (such as the 3-D seismic revolution). To a large extent, this improvement has been accomplished on the coattails of advances in computing and related disciplines. These advances have allowed cost-effective implementation of methods that exploit our steadily increasing understanding of geophysical theory in ever increasingly realistic earth models. As a result, geophysical methods can now provide clearer images at greater distances with better resolution and signal-to-noise ratio than ever before. Consequently, explorationists are far more effective now than they were a decade ago. Predictions that earth resources will be depleted in the near future are continually being proven wrong, in no small part due to the consequences of geophysical progress that was not anticipated by the prognosticators. We expect this trend to continue into the twenty-first century with many exciting avenues for improvement available before us. One caution and caveat: The precipitous drop in funding for research that has occurred, although temporarily boosting profits, will translate into a slower rate of improvement of geophysical data in the future, which will ultimately cause poorer success rates. Whether investors of the future do or do not recognize that diminishing “yields” are a direct consequence of the conscious decision to inadequately fertilize the fields of research at the present time remains to be seen.
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 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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".