A 300 m long depth profile of metabolic activity of sulfate‐reducing bacteria in the continental margin sediments of South Australia (ODP Site 1130) derived from inverse reaction‐transport modeling
Bibliographic record
Abstract
Reaction‐transport modeling of dissolved species in interstitial water allows for the inversion of transport processes and thus facilitates the detailed investigation of signals which are usually blurred by diffusion and advection. Here I present a case study from the South Australian continental margin (ODP Leg 182, Site 1130) where I use reaction‐transport modeling to derive a depth transect of volumetric sulfate reduction rates. Site 1130, located on the shelf slope in 500 m deep water, is of special interest as an upwelling sulfate‐rich brine allows for an extended sulfate reduction zone which reaches to a depth of at least 300 mbsf. The obtained reduction rates vary from 600 pmol/cm−3 yr−1 at 30 mbsf to 63 pmol/cm−3 yr−1 at 300 mbsf. The depth‐integrated sulfate consumption equals 65 × 10−6 mol/yr cm−2, which is similar to other shelf slope settings without advecting sulfate. This suggests that the primary control on sulfate reduction rates is organic matter reactivity, rather than sulfate availability. However, similar to other ODP Leg 182 sites, the interstitial water chemistry in the upper 30 mbsf is inconsistent with a diffusive/advective transport system. While the actual process causing this remains elusive, pyrite burial rates from this zone suggest that sulfate reduction rates in this zone are at least 60 times higher than those derived from reaction‐transport modeling assuming diffusion and advection alone.
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 | 0.000 | 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".