Control of particulate manganese (Mn) cycling in halocline Arctic Ocean waters by putative Mn‐oxidizing bacterial dynamics
Bibliographic record
Abstract
Abstract Particulate Mn, given its high adsorptive capacity and oxidation potential, has profound impacts on the cycling of various trace elements and organic matter in the ocean. Moreover, particulate Mn acts as a sink (via oxidation and adsorption) or as a source (via remineralization and photoreduction) term of bioactive dissolved Mn(II). In the Canadian Arctic Ocean, particulate Mn distributions in the water column revealed the presence of distinctively high particulate Mn concentrations and an overwhelming dominance of the non‐lithogenic component to the bulk particulate Mn pool. This phenomenon is of particular importance in halocline waters in the Canada Basin, the Canadian Arctic Archipelago and Baffin Bay, and near‐bottom samples in Baffin Bay. Enhanced microbially‐mediated Mn oxidation in the water column is suggested as the main mechanism driving the non‐lithogenic dominance. Indeed, the microbial community composition data associated with high non‐lithogenic particulate Mn (i.e., Mn oxides) display a high relative abundance of taxa (e.g., f.Pirellulaceae, o.Phycisphaerales, f.Cryomorphaceae, g. Moritella) that have been identified in Mn oxide enriched environments. Furthermore, numerous taxa identified in the Canada Basin halocline water, where non‐lithogenic particulate Mn peaked, are phylogenetically related to known (cultured) Mn‐oxidizing bacteria (MnOB; e.g., Rhodobacteraceae, Oceanospirillaceae, Rhizobiaceae, and other Alphaproteobacteria). Putative MnOB appears to proliferate in certain water masses having a unique set of environmental conditions: low light intensity—alleviating photoinhibition—and high dissolved Mn concentrations, the main drivers known to influence MnOB dynamic, and hence, Mn oxidation.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".