Evaluating episodicity of high-temperature venting within seafloor hydrothermal vent fields
Bibliographic record
Abstract
Hydrothermal episodicity refers to the cycle of cessation and reactivation of hydrothermal venting at the seafloor and is often considered a common characteristic of high-temperature seafloor hydrothermal systems. With few exceptions, evidence for episodicity at the vent field scale and at timescales of hydrothermal systems (1000s to 100,000s of years) is primarily derived from interpretation of the age distribution of rock samples collected from hydrothermal vent fields and dated using U-series disequilibrium techniques. Using this approach, significant age gaps between dated samples have been interpreted as hiatuses in the venting of fluids that form the deposits that accumulate at the vent fields. Here, we use Monte Carlo simulations to show that the maximum time gaps in the observed age distributions are similar to those predicted by modeling random sampling of a logarithmic age distribution. These simulation results indicate that large time gaps between dated samples do not indicate episodic venting, and/or the numbers of dated samples are not high enough to confidently distinguish between continuous and episodic venting. The lack of geochronological evidence for episodicity suggests that, although fluid temperature and composition within a vent field can vary over time, hydrothermal fluid circulation may be continuous over the lifespans of vent fields.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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 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".