Particle cycling rates at Station P as estimated from the inversion of POC concentration data
Bibliographic record
Abstract
Particle cycling rates in marine systems are difficult to measure directly, but of great interest in understanding how carbon and other elements are distributed throughout the ocean. Here, rates of particle production, aggregation, disaggregation, sinking, remineralization, and transport mediated by zooplankton diel vertical migration were estimated from size-fractionated measurements of particulate organic carbon (POC) concentration collected during the NASA EXport Processes in the Ocean from RemoTe Sensing (EXPORTS) cruise at Station P in summer 2018. POC data were combined with a particle cycling model using an inverse method. Our estimates of the total POC settling flux throughout the water column are consistent with those derived from thorium-234 disequilibrium and sediment traps. A budget for POC in two size fractions, small (1–51 µm) and large (> 51 µm), was produced for both the euphotic zone (0–100 m) and the upper mesopelagic zone (100–500 m). We estimated that POC export at the base of the euphotic zone was 2.2 ± 0.8 mmol m−2 d−1, and that both small and large particles contributed considerably to the total export flux along the water column. The model results indicated that throughout the upper 500 m, remineralization leads to a larger loss of small POC than does aggregation, whereas disaggregation results in a larger loss of large POC than does remineralization. Of the processes explicitly represented in the model, zooplankton diel vertical migration is a larger source of large POC to the upper mesopelagic zone than the convergence of large POC due to particle sinking. Positive model residuals reveal an even larger unidentified source of large POC in the upper mesopelagic zone. Overall, our posterior estimates of particle cycling rate constants do not deviate much from values reported in the literature, i.e., size-fractionated POC concentration data collected at Station P are largely consistent with prior estimates given their uncertainties. Our budget estimates should provide a useful framework for the interpretation of process-specific observations obtained by various research groups in EXPORTS. Applying our inverse method to other systems could provide insight into how different biogeochemical processes affect the cycling of POC in the upper water column.
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 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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".