Evaluation of new and net community production estimates by multiple ship-based and autonomous observations in the Northeast Pacific Ocean
Bibliographic record
Abstract
New production (NP) and net community production (NCP) measurements are often used as estimates of carbon export potential from the mixed layer of the ocean, an important process in the regulation of global climate. Diverse methods can be used to measure NP and NCP, from research vessels, autonomous platforms, and remote sensing, each with its own set of benefits and uncertainties. The various methods are rarely applied simultaneously in a single location, limiting our ability for direct comparisons of the resulting measurements. In this study, we evaluated NP and NCP from thirteen independent datasets collected via in situ, in vitro, and satellite-based methods near Ocean Station Papa during the 2018 Northeast Pacific field campaign of the NASA project EXport Processes in the Ocean from RemoTe Sensing (EXPORTS). Altogether, the datasets indicate that carbon export potential was relatively low (median daily averages between −5.1 and 12.6 mmol C m−2 d−1), with most measurements indicating slight net autotrophy in the region. This result is consistent with NCP estimates based on satellite measurements of sea surface temperature and chlorophyll a. We explored possible causes of discrepancies among methods, including differences in assumptions about stoichiometry, vertical integration, total volume sampled, and the spatiotemporal extent considered. Results of a generalized additive mixed model indicate that the spatial variation across platforms can explain much of the difference among methods. Once spatial variation and temporal autocorrelation are considered, a variety of methods can provide consistent estimates of NP and NCP, leveraging the strengths of each approach.
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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 | 0.004 | 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 | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".