Data report: water activity of the deep coal-bearing basin off Shimokita from IODP Expedition 337
Bibliographic record
Abstract
Water activity (A w ) is one of the physicochemical properties that may influence microbial activity in deep subseafloor environments; however, A w for subseafloor sediments has never been examined, even at shallow depths.This study investigated A w data obtained from core samples collected during Integrated Ocean Drilling Program Expedition 337 in the deep-water coal-bearing basin off Shimokita, Japan.A w did not show any depth dependence and was relatively low in coal-bearing layers.A w at depths of 0-2466 meters below seafloor ranged from 0.95 to 0.98, which is quite high and well suited to sustaining microorganisms.A w for sedimentary rocks was less affected by lithology and porosity than it was by the NaCl concentration and degree of fluid saturation.In addition, the A w measurements performed in this study yielded results that corresponded closely with values estimated using Raoult's law and interstitial water chemistry.It therefore appears that A w for deep-marine sediments is strongly affected by pore water chemistry.The low A w anomaly in the coalbed unit is considered to be due to contamination by drilling mud and fluid into core samples.where RH (%) is the relative humidity of air and A w ranges from 0 (no water) to 1 (pure water).A high A w implies that an environment is more habitable to microbes; most microbes cannot proliferate at water activity values below 0.9, and even extremophiles,
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".