Estimating Primary Production of Picophytoplankton Using the Carbon-Based Ocean Productivity Model: A Preliminary Study
Bibliographic record
Abstract
Picophytoplankton are acknowledged to contribute significantly to primary production in the ocean while now the method to measure primary production of picophytoplankton at large scales is not yet well established. Although the traditional 14C method and new technologies based on the use of stable isotopes (e.g. 13C) can be employed to accurately measure in situ primary production of picophytoplankton, the time-consuming and labor-intensive shortage of these methods constrain their application in a survey on large spatiotemporal scales. To overcome this shortage, a modified carbon-based ocean productivity model (CbPM) is proposed for estimating the primary production of picophytoplankton whose principle is based on the group-specific abundance, cellular carbon conversion factor and temperature derived growth rate of picophytoplankton. Comparative analysis showed that the estimated primary production of picophytoplankton using CbPM method is significantly and positively related (r2= 0.53, P<0.001, n= 171) to the measured 14C uptake. This significant relationship suggests that CbPM has the potential to estimate the primary production of picophytoplankton over large spatial and temporal scales. Currently this model application may be limited by the use of invariant cellular carbon conversion factor and the relatively small data sets to validate the model which may introduce some uncertainties and biases. Model performance will be improved by the use of variable conversion factors and the larger data sets representing diverse growth conditions. Finally, we apply the CbPM-based model on the collected data during four cruises in the Bohai Sea in 2005. Model-estimated primary production of picophytoplankton ranged from 0.1 to 11.9, 29.9 to 432.8, 5.5 to 214.9 and 2.4 to 65.8 mg C m–2 d–1 during March, June, September and December, respectively. This study shed light on the estimation of global primary production of picophytoplankton using carbon-based production model.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".