Proliferating particle surface area via microbial decay has profound consequences for remineralisation rate: a new approach to modelling the degradation of sinking detritus in the ocean
Bibliographic record
Abstract
Abstract Sinking detritus particles in the ocean help to regulate global climate by transporting organic carbon into deep waters where it is sequestered from the atmosphere. The rate at which bacteria remineralise detritus influences how deep particles sink and the length-scale of carbon sequestration. Conventional marine biogeochemical models typically represent particles as smooth spheres where remineralisation causes surface area (SA) to progressively shrink over time. In contrast, we propose that particle SA increases during degradation as microbial ectoenzymes cause a roughening of surfaces in a process similar to acid etching on previously smooth glass or metal surfaces. This concept is investigated using a novel model, SAMURAI (Surface Area Modelling Using Rubik As Inspiration), in which the biomass of individual particles is represented as a 3D matrix of cubical sub-units that degrades by progressive removal of sub-units that have faces in contact with the external environment. The model rapidly generates microscale rugosity (roughness) that profoundly increases total SA, giving rise to biomass-specific remineralisation rates that are approximately double those of conventional models. Faster remineralisation means less carbon penetrates the ocean’s interior, diminishing carbon sequestration in deep waters. Results indicate that both SA and microbial remineralisation are highly dynamic, as well as exhibiting large variability associated with particles of different porosities. Our work highlights the need for further studies, both observational and modelling, to investigate particle SA and related microbial dynamics in order to reliably represent the role of ocean biology in global biogeochemical models.
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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 | 0.001 | 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) | 0.000 | 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".