Consistent Relationships Among Productivity Rate Methods in the NE Subarctic Pacific
Bibliographic record
Abstract
Abstract Phytoplankton photosynthesize in surface waters, exporting organic carbon to depth through the biological pump. Quantifying productivity and the export of carbon is important to understanding the global carbon cycle and predicting its future changes. An issue in quantifying rates is that the many existing methods are not all equivalent, making comparisons between studies using different methods challenging. Our goal is to compare in situ and in vitro methods in order to identify where methods agree in the NE subarctic Pacific. During this study, we measured productivity using two in situ methods (oxygen/argon ratio and triple oxygen isotope mass balance approaches) and four in vitro methods (13C, , , and H218O uptake rates through incubations), and compared the results with one satellite‐based productivity estimate. The in situ carbon export method was consistently higher than the in vitro method, likely due to dissolved organic matter release not included in our incubation measurements. Upwelling bringing low‐O2 water to the surface and the interaction between bloom dynamics and a method's inherent time of integration cause outliers from the relationship. In contrast, in situ and in vitro methods for estimating gross primary production were consistent across a wide range in rates. We find that chlorophyll‐a concentration is strongly related to many of our measured rates. Satellite estimates of primary production are consistently different from 13C incubations. Our identification of consistent trends and causes for disagreement will allow observations from one method to be converted to another, facilitating future comparisons across studies.
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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 | 0.003 | 0.007 |
| 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 | 0.001 | 0.000 |
| Open science | 0.001 | 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 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".