Gas Hydrates and Magnetism: Surveying and Diagenetic Analysis
Bibliographic record
Abstract
Geochemical processes associated with gas-hydrate formation lead to the growth of iron sulphides, which have a geophysically measurable magnetic signature. Detailed magnetic investigation and complementary petrological observations were undertaken on cores from the permafrost setting Mackenzie Delta Mallik region (Northwest Territories) and the marine setting IODP Expedition 311 cores from the Cascadia margin off Vancouver Island. These magnetic measurements provide stratigraphic profiles, which reveal fine scale variations in lithology, magnetic grain size, and pore fluid geochemistry. The highest magnetic susceptibility values are observed in strata preserve high initial concentrations of detrital magnetite, such as glacial deposits. The lowest values of magnetic susceptibility are observed in which iron has been reduced to paramagnetic pyrite, formed in settings with high methane and sulphate flux such as at methane vents. Enhanced values of magnetic susceptibility characterize the introduction of the ferrimagnetic iron sulphide minerals greigite (Skinner et al., 1964) and smythite (Erd et al., 1957). These magnetic minerals are mostly found immediately adjacent to the sedimentary horizons, which host the gas hydrate, and their textures and compositions indicate rapid disequilibrium crystallization. These observations result from the unique physical and geochemical properties of the environment in which gas hydrates form; methane is available to fuel microbiological activity and pore water solutes concentrate during gas-hydrate formation. In these conditions, iron sulphides bacterially precipitate from solute rich brines. Thus, magnetic surveying techniques can help delineate anomalies related to gas-hydrate deposits, and magnetic logging of wells and core samples provide information on the original lithology and diagenesis caused by gas-hydrate formation.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| 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) | 0.001 | 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".