Eingeschlossene Ressource. Methan in natuerlichen Gashydraten
Bibliographic record
Abstract
The presence of natural gas hydrates at all active and passive continental margins has been proven. Their global occurrence as well as the fact that huge amounts of methane and other lighter hydrocarbons are stored as natural gas hydrates has led to the idea of using hydrate bearing sediments as an energy resource. However, natural gas hydrates remain stable as long as they are in mechanical, thermal and chemical equilibrium with their environment. Thus, for the production of gas from hydrate bearing sediments, at least one of these equilibrium states must be disturbed e.g. by depressurization, heating or addition of chemicals such as CO2. Depressurization, thermal or chemical stimulation may be used alone and also in combination. Producing hydrocarbons from hydrate bearing sediments by CO2 injection, in particular, suggests the potential of an almost greenhouse gas neutral use of this unconventional natural gas resource. In any case, the exploitation of natural gas hydrates is a technical challenge. The GFZ was already involved in the first real production test at the Mallik testsite in Canada in 2001/2002 using thermal stimulation. Within the framework of the German joint research project SUGAR scientists from GFZ German Research Centre for Geosciences developed and tested an innovative method using in situ combustion for thermal stimulation on a pilot plant scale. Preliminary results are quite promising: this kind of thermal stimulation, combined with depressurization may be an efficient method to produce gas form natural gas hydrate deposits.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.112 | 0.101 |
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".