A Behavioristic Approach to the Design of a Digital Model of Human Communication
Bibliographic record
Abstract
Deep-sea hydrothermal vents are important in global biogeochemical cycles, providing biological oases at the sea floor that are supported by the thermal and chemical flux from the Earth's interior. As hot, acidic and reduced hydrothermal fluids mix with cold, alkaline and oxygenated sea water, minerals precipitate to form porous sulphide–sulphate deposits. These structures provide microhabitats for a diversity of prokaryotes that exploit the geochemical and physical gradients in this dynamic ecosystem. It has been proposed that fluid pH in the actively venting sulphide structures is generally low (pH < 4.5), yet no extreme thermoacidophile has been isolated from vent deposits. Culture-independent surveys based on ribosomal RNA genes from deep-sea hydrothermal deposits have identified a widespread euryarchaeotal lineage, DHVE2 (deep-sea hydrothermal vent euryarchaeotic 2) Despite the ubiquity and apparent deep-sea endemism of DHVE2, cultivation of this group has been unsuccessful and thus its metabolism remains a mystery. Here we report the isolation and cultivation of a member of the DHVE2 group, which is an obligate thermoacidophilic sulphur- or iron-reducing heterotroph capable of growing from pH 3.3 to 5.8 and between 55 and 75 °C. In addition, we demonstrate that this isolate constitutes up to 15% of the archaeal population, providing evidence that thermoacidophiles may be key players in the sulphur and iron cycling at deep-sea vents.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 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".