Carbon: Chlorophyll Ratios and Net Primary Productivity of Subarctic Pacific Surface Waters Derived From Autonomous Shipboard Sensors
Bibliographic record
Abstract
Abstract We present optically derived estimates of phytoplankton carbon (Cphyto) and chlorophyll a concentration (Chl) across a wide range of productivity and hydrographic regimes in the Subarctic Pacific Ocean. Our high‐frequency measurements capture changes in Cphyto and Chl across regional gradients in macronutrient and micronutrient limitations and submesoscale hydrographic frontal zones. Throughout the majority of our survey region, carbon to chlorophyll ratios (Cphyto:Chl) ranged between 50 and 100. Lower values (10–20) were constrained to the highly productive coastal upwelling system along Vancouver Island, whereas higher estimated values (>200) were found directly off the southern British Columbia continental shelf. Further offshore, Cphyto:Chl was less variable, ranging from 50 to 80 in high nutrient low Chl waters in June and from 80 to 120 in the Gulf of Alaska in July. Much of the variability in Cphyto:Chl throughout the study region could be explained by mixed‐layer light levels (i.e., photoacclimation), with additional variability attributed to nutrient‐controlled changes in phytoplankton growth rates in some regions. Elevated Cphyto:Chl ratios resulting from apparent nutrient stress were found in areas of low macronutrient concentrations. In contrast, iron‐limited waters exhibited Cphyto:Chl ratios lower than predicted from the photoacclimation model. Applying the carbon‐based production model, we derived Cphyto and Chl‐based estimates of net primary productivity, which showed good coherence with independent 14C uptake measurements. Our results highlight the utility of ship‐board optical data to examine phytoplankton physiological ecology and productivity in surface marine waters.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 |
| 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".