Bibliographic record
Abstract
Subsurface microbial presence and involvement in geochemical transformations was reported decades ago (Farrell and Turner, 1931; Kuznetsov et al., 1963; Lipman, 1931), and an acceleration in subsurface research has occurred over the last decade. Much of this research has been collaborative and interdisciplinary, involving the efforts of microbiologists, geochemists, hydrologists, drilling and mining experts, as well as program support, and has thus facilitated rapid progress. A primary incentive has been the potential for microbiota to clean up contaminated underground environments for the protection or purification of water supplies through bioremediation (Ghiorse and Wilson, 1988; Madsen and Ghiorse, 1993; Balkwill et al., 1994). Not long ago most scientists firmly believed that life was absent below some fairly shallow threshold depth (Ghiorse and Wilson, 1988). Microbiota have now been recovered at depths greater than 9000 ft below the surface (Boone et al., 1995). Additionally, investigations of deep ocean sediments indicate that they are teeming with microbial life (Parks et al., 1994). Microorganisms have been investigated in both shallow and deep aquifer systems and associated sediments (Balkwill and Ghiorse, 1985; Hirsch, 1992). Sampling techniques have included both drilling/coring procedures, and mining in rock, ore, and salt deposits (see Chapters 3 and 4). European, Canadian, and American scientists have also studied the microbiology of the terrestrial subsurface for the practical reason that radioactive and other waste repositories are often built underground and microbes inhabiting those environments may impact the integrity of the waste storage facilities (see Chapters 15 and 16, and Proceedings of the 7th Annual International High Level Radioactive Waste Management Meetings, Las Vegas, 1996).
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.035 | 0.009 |
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".