Ecology and biogeochemistry of contrasting trophic environments in the South East Pacific by carbon isotope ratios on lipid biomarkers
Bibliographic record
Abstract
Abstract. The distribution of lipid biomarkers and their carbon isotope composition was investigated on suspended particles from different contrasting trophic environments at six sites in the South East Pacific. High algal biomass with diatom-related lipids was characteristic in the upwelling zone, whereas haptophyte lipids were proportionally most abundant in the nutrient-poor settings of the centre of the South Pacific Gyre and on its easter edge. Dinoflagellate–sterols were minor contributors in all of the studied area and cyanobacteria-hydrocarbons were at maximum in the high nutrient low chlorophyll regime of the subequatorial waters at near the Marquesas archipelago. The taxonomic and spatial variability of the relationships between carbon photosynthetic fractionation and environmental conditions for four specific algal taxa (diatoms, haptophytes, dinoflagellates and cyanobacteria) was also investigated. The carbon isotope fractionation factor (εp) of the diatom marker varied over a range of 16‰ along the different trophic systems. In contrast, εp of dinoflagellate, cyanobacteria and alkenone markers varied only by 7–10‰. The low fractionation factors and small variations between the different phytoplankton markers measured in the upwelling area likely reveals uniformly high specific growth rates within the four phytoplankton taxa, and/or that transport of inorganic carbon into phytoplankton cells may not only occur by diffusion but by other carbon concentrating mechanisms (CCM). In contrast, in the oligotrophic zone, i.e. gyre and eastgyre, relatively high εp values, especially for the diatom marker, indicate diffusive CO2 uptake by the eukaryotic phytoplankton. At these nutrient-poor sites, the lowest εp values for haptophytes, dinoflagellates and cyanobacteria infer higher growth rates compared to diatoms.
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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 | 0.001 |
| 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".