Modelling of the gas hydrate potential in Svalbard’s fjords
Bibliographic record
Abstract
Large amounts of methane are trapped as natural gas hydrate (NGH) in the sediments of the Arctic. Unlike NGH provinces offshore west of Svalbard (Vestnesa Ridge), NGH potential in Svalbard’s fjords and near-shore environment is poorly constrained. In this study we modelled the NGH stability zone (GHSZ) to determine the NGH formation potential within the fjords of Svalbard. We applied a nearest neighbour interpolation method to dynamically derive statistical bottom-water temperature (BWT) trends from available CTD data. The BWT trends along with available geothermal gradient data constrained Svalbard-wide sub-bottom thermobaric models suitable for GHSZ modelling in the near subsurface. Analyses of source rock and fluid seepage data in Isfjorden, including 15 newly identified acoustic gas flares, indicate an active petroleum system with fluid migration reaching the seafloor with significant contributions of higher-order hydrocarbons to the gas feed. A GHSZ is predicted for most fjords at mean BWT conditions and 95:5 methane:ethane gas compositions. Suitable conditions for pure methane NGH formation are only met in the deepest parts of Isfjorden, Hinlopenstretet, Kross- and Kongsfjorden, and Rijpfjorden. Temporal constraints implemented along the well-defined Isfjorden transect indicated a notable negative response to water column warming. The predicted GHSZ across Svalbard’s fjords is likely to disappear over the next few decades.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".