A numerical model for the formation of gas hydrate below the seafloor
Bibliographic record
Abstract
We develop a numerical model to predict the volume and distribution of gas hydrate in marine sediments. We consider the environment of a deep continental margin where sedimentation adds organic material to the region of hydrate stability. Conversion of the organic material to methane by bacteria promotes hydrate formation and depletes the supply of organic carbon. We derive mass balance equations for the volume of hydrate and gas bubbles in the sediments and account for the changing concentration of dissolved methane and salts in the pore fluid. The effects of sediment compaction and the associated fluid flow are explicitly modeled. Allowances for deeper sources of fluid are also described, though we focus on the case of an idealized passive margin where carbon is input solely through sedimentation. The numerical calculations indicate that the key parameters in this model are the rate of sedimentation, the quantity and quality of the organic material, and a rate constant that characterizes the vigor of biological productivity. Model predictions for conditions that are representative of the Blake Ridge are compared with observations from Ocean Drilling Program Leg 164. We obtain a very good match to the observed chlorinity profile, including the region below the stability zone, without invoking any extraneous sources of freshening. We also predict that hydrate is unlikely to occupy more than 7% of the pore volume, in good agreement with observed estimates.
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 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".