CbPM estimates of net primary production in the North Atlantic from profiling floats and satellites diverge seasonally due to fluorescence and vertical extrapolation effects
Bibliographic record
Abstract
Abstract Marine net primary production (NPP), defined as the difference between gross production and phytoplankton respiration, is often estimated using algorithms applied to remote sensing data. While assumptions are needed to extend surface satellite observations through depth, some NPP algorithms, like the Carbon‐based Productivity Model (CbPM), have been adapted to vertically resolved data collected by autonomous profiling floats. Such applications eliminate the need for vertical extrapolation but introduce challenges related to float measurements of fluorescence rather than chlorophyll‐ a (Chl‐ a ; required CbPM input). This study analyzes over a decade of float observations from the North Atlantic to estimate NPP using CbPM and quantify its sensitivity to different input parameters: (a) fluorescence versus Chl‐ a , (b) vertically extrapolated versus depth‐resolved information, and (c) in situ versus remote observations of the first optical depth—the impacts of which vary seasonally and regionally. In higher latitude waters, converting float fluorescence to Chl‐ a using a novel correction based on satellite data produces significantly smaller NPP estimates at seasonal and annual timescales. In contrast, extrapolation and platform‐related differences largely compensate when integrated vertically and annually, such that cumulative annual depth‐integrated NPP (iNPP) estimates computed with fluorescence‐corrected float measurements are statistically indistinguishable from those extrapolated from satellite observations. These effects are reversed in the subtropics: discrepancies due to fluorescence compensate vertically and annually, whereas annual iNPP estimates from depth‐resolved float measurements significantly outweigh those of satellites. Seasonal changes to the sign, timing, and vertical structure of NPP discrepancies suggest persistent sub‐seasonal disagreement between platforms, highlighting knowledge gaps in understanding NPP.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".