Compositional variability in the ascending fluxes from a hydrothermal plume
Bibliographic record
Abstract
Sequentially sampling sediment traps set with 33 day sampling intervals were deployed with current meters on three moorings in the northeast Pacific Ocean between July 1994 and May 1995. One mooring was deployed near the Main Vent field on Endeavour Ridge (On‐Axis site, 47°57.0′N, 129°05.7′W), a second, 3 km west of the Main vent site (West site), and the third, 43 km northeast of the Main vent site (East or background site). Ascending and descending particles were collected near 1600 and 2000 m depth, well above and within the top of laterally spreading hydrothermal plumes. The elemental composition of particles was used to evaluate their origins: biogenic fluxes were indicated by elevated Ca or Si, hydrothermal fluxes by elevated Fe, Mn, and Cu, and lithogenic fluxes by elevated Ti. We link temporal variability in both the ascending and descending particle composition and flux to variations in lateral transport of hydrothermal constituents and to seasonal drawdown of hydrothermal plume particles by biogenic material from the upper ocean. The relatively low hydrothermal Fe content of ascending material late in the experiment is thought to be due to uptake by descending biogenic material. These results suggest that seasonal productivity and particle export from the ocean surface can modulate the hydrothermal flux of elements to the waters above and to the sediments below.
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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 | 0.000 | 0.000 |
| 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".