Nucleation and Growth of Hydrate in Marine Environments
Bibliographic record
Abstract
An experimental study of nucleation and growth of carbon dioxide hydrate in the natural porous medium is used as an analog for understanding how hydrate forms in marine environments. Detection of hydrate is achieved by monitoring the bulk electrical resistance in the porous medium. Carbon dioxide is chosen as the hydrate-forming gas because it ionizes in water to provide with the main source of charged particles for conduction. Since hydrate formation causes a decrease in the dissolved gas concentration (and hence the number of ions in solution), we use the measured changes in the conductivity over time to obtain quantitative information about rates of nucleation and growth. Our experiments indicate that the rate limiting process for hydrate formation is nucleation. Once hydrate crystals nucleate, their subsequent growth is controlled by mass transport. This means that the local equilibrium in the immediate vicinity of hydrate crystals is readily established and kinetics of growth and dissociation is mainly regulated by diffusion of gas. We have developed a theoretical model to recover nucleation rates from our experimental data. Estimates of these rates for carbon dioxide hydrate are used to infer nucleation rates for methane hydrate, which is the most abundant in the natural environment. The results are applied to marine situations to show that nucleation occurs well above BSR when methane migrates into the hydrate stability zone from below. Hydrate formation is only possible if the stability zone is deep enough to ensure that sufficient overcooling can be achieved in the sediments. During the Paleocene, when the seafloor was warmer and the stability zone was narrower, nucleation of hydrate in sediments would have been much less probable than it is today. A similar difficulty may arise in shallow water where the lower pressure yields a narrow stability zone. This may explain why hydrates are rarely found in the minimum water depth of 500-600 meters even though thermodynamic conditions predict stability.
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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 | 0.000 | 0.000 |
| 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) | 0.000 | 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".