Decoupling of ΔO <sub>2</sub> ∕Ar and particulate organic carbon dynamics in nearshore surface ocean waters
Bibliographic record
Abstract
We report results from two Lagrangian drifter surveys off the Oregon coast, using continuous shipboard sensors to estimate mixed-layer gross primary productivity (GPP), community respiration (CR), and net community production (NCP) from variations in biological oxygen saturation (ΔO 2 ∕Ar) and optically derived particulate organic carbon (POC). At the first drifter survey, conducted in a nearshore upwelling zone during the development of a microplankton bloom, net changes in ΔO 2 ∕Ar and [POC] were significantly decoupled. Differences in GPP and NCP derived from ΔO 2 ∕Ar (NCPO2/Ar) and POC (NCP POC ) time series suggest the presence of large POC losses from the mixed layer. At this site, we utilized the discrepancy between NCPO2/Ar and NCP POC , and additional constraints derived from surface water excess nitrous oxide (N 2 O), to evaluate POC loss through particle export, DOC production, and vertical mixing fluxes. At the second drifter survey, conducted in lower-productivity, density-stratified offshore waters, we also observed offsets between ΔO 2 ∕Ar and POC-derived GPP and CR rates. At this site, however, net [POC] and ΔO 2 ∕Ar changes yielded closer agreement in NCP estimates, suggesting a tighter relationship between production and community respiration, as well as lower POC loss rates. These results provide insight into the possibilities and limitations of estimating productivity from continuous underway POC and ΔO 2 ∕Ar data in contrasting oceanic waters. Our observations support the use of diel POC measurements to estimate NCP in lower-productivity waters with limited vertical carbon export and the potential utility of coupled O 2 and optical measurements to estimate the fate of POC in high-productivity regions with significant POC export.
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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".