Contribution of picoplankton to the total particulate organic carbon (POC) concentration in the eastern South Pacific
Bibliographic record
Abstract
Abstract. Prochlorococcus, Synechococcus, picophytoeukaryotes and bacterioplankton abundances and contributions to the total particulate organic carbon concentration (POC), derived from the total particle beam attenuation coefficient (cp), were determined across the eastern South Pacific between the Marquesas Islands and the coast of Chile. All flow cytometrically derived abundances decreased towards the hyper-oligotrophic centre of the gyre and were highest at the coast, except for Prochlorococcus, which is not detected under eutrophic conditions. Temperature and nutrient availability appeared important in modulating picophytoplankton abundance, according to the prevailing trophic conditions. Although the non-vegetal particles tended to dominate the cp signal everywhere along the transect (50 to 83%), this dominance seemed to weaken from oligo- to eutrophic conditions, the contributions by vegetal and non-vegetal particles being about equal under mature upwelling conditions. Spatial variability in the vegetal compartment was more important than the non-vegetal one in shaping the water column particulate attenuation coefficient. Spatial variability in picophytoplankton biomass could be traced by changes in both Tchla and cp. Finally, picophytoeukaryotes contributed with ~38% on average to the total integrated phytoplankton carbon biomass or vegetal attenuation signal along the transect, as determined by direct size measurements on cells sorted by flow cytometry and optical theory. The role of picophytoeukaryotes in carbon and energy flow would therefore be very important, even under hyper-oligotrophic conditions.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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".