Hydrothermal venting in magma deserts: The ultraslow‐spreading Gakkel and Southwest Indian Ridges
Bibliographic record
Abstract
Detailed hydrothermal surveys over ridges with spreading rates of 50–150 mm/yr have found a linear relation between spreading rate and the spatial frequency of hydrothermal venting, but the validity of this relation at slow and ultraslow ridges is unproved. Here we compare hydrothermal plume surveys along three sections of the Gakkel Ridge (Arctic Ocean) and the Southwest Indian Ridge (SWIR) to determine if hydrothermal activity is similarly distributed among these ultraslow ridge sections and if these distributions follow the hypothesized linear trend derived from surveys along fast ridges. Along the Gakkel Ridge, most apparent vent sites occur on volcanic highs, and the extraordinarily weak vertical density gradient of the deep Arctic permits plumes to rise above the axial bathymetry. Individual plumes can thus be extensively dispersed along axis, to distances >200 km, and ∼75% of the total axial length surveyed is overlain by plumes. Detailed mapping of these plumes points to only 9–10 active sites in 850 km, however, yielding a site frequency Fs, sites/100 km of ridge length, of 1.1–1.2. Plumes detected along the SWIR are considerably less extensive for two reasons: an apparent paucity of active vent fields on volcanic highs and a normal deep‐ocean density gradient that prevents extended plume rise. Along a western SWIR section (10°–23°E) we identify 3–8 sites, so Fs = 0.3–0.8; along a previously surveyed 440 km section of the eastern SWIR (58°–66°E), 6 sites yield Fs = 1.3. Plotting spreading rate (us) versus Fs, the ultraslow ridges and eight other ridge sections, spanning the global range of spreading rate, establish a robust linear trend (Fs = 0.98 + 0.015us), implying that the long‐term heat supply is the first‐order control on the global distribution of hydrothermal activity. Normalizing Fs to the delivery rate of basaltic magma suggests that ultraslow ridges are several times more efficient than faster‐spreading ridges in supporting active vent fields. This increased efficiency could derive from some combination of three‐dimensional magma focusing at volcanic centers, deep mining of heat from gabbroic intrusions and direct cooling of the upper mantle, and nonmagmatic heat supplied by exothermic serpentinization.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".