On the compliance method and the assessment of three-dimensional seafloor gas hydrate deposits
Bibliographic record
Abstract
A R Y Marine gas hydrates, prevalent in offshore sediments in Canada and Japan, are a possible hydrocarbon resource, a hazard to drilling and the source of a major greenhouse gas.Quantitative estimates of hydrate concentrations in deep sea sediment are difficult to obtain by conventional methods.Our group has sought novel techniques specifically designed for assessment such as the compliance method where ocean surface gravity waves are used as a source.Compliance is the transfer function between the vertical displacement of the seafloor and the corresponding pressure expressed as a function of wavelength.It is sensitive to the elastic parameters of the underlying sediments, particularly the shear modulus which is probably increased in zones containing hydrate due to cementation.Our group has demonstrated a connection between a compliance measurement and the amount of hydrate present in the hydrate stability zone for layered models.Here, we develop a 3-D numerical finite-difference code using control volume discretization to predict the compliance response over non-layered structures.Among the features of the algorithm are an ability to handle sharp contrasts in elastic moduli with a low usage of computer memory.The compliance 'anomaly' over such structures has a signature and resolution not unlike the corresponding gravity anomaly.The average response over heterogeneous structures is sensitive to the bulk hydrate content but not the connectivity pattern.Gravity waves with different polarizations crossing markedly anisotropic structures produce statistically the same average stiffness value.
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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 | 0.003 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".