Overview Article Photosynthetic carbon allocation: Effects of planktivorous fish and nutrient enrichment
Bibliographic record
Abstract
Abstract. The proportions of lipid, polysaccharide and protein profoundly influence the nutritional value of food for planktonic organisms. Here, we investigated the ef-fects of nutrients (N and P) and planktivorous fish (Phox-inus sp.) on the partitioning of photosynthetically fixed 14C-labelled bicarbonate into macromolecules (lipids, polysaccharides and proteins) and low molecular weight (LMW) metabolites of epilimnetic phytoplankton living in large (8 m diameter) enclosures from July to mid-Oc-tober 1991. The enclosures were situated in an olig-otrophic lake (north of Montreal, Canada) in both shallow (3.5 m) and deep (11 m) locations. The fractions were cal-culated using the sequential extraction of 14C-labeled phytoplankton samples incubated for short duration (~2 hours) at maximum photosynthetic rates. Labelling pat-terns of all end products exhibited great temporal varia-tions. Overall, the LMW, polysaccharide, lipid and pro-tein fractions accounted for 32.1 ± 10.5%, 31.0 ± 8.8%, 19.2 ± 5.3 % and 17.8 ± 6.1 % of the total 14C fixed respectively. Percent 14C in the LMW fraction was, in general, lower in the nutrient-enriched enclosure and was usually lower in the deep than the shallow enclosures. The effect of fish on the labelling pattern of LMW varied throughout the sampling period but, overall, its impact was non-significant. The proportions of 14C into lipids and polysaccharides did not show the expected increase under nutrient deficiency. In contrast, the polysaccharide fraction increased, for the most part, with nutrient enrichment and was higher in the deep than the shallow enclosures with little changes in the presence of plank-tivorous fish. Incorporation of 14C into lipids remained
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.020 | 0.005 |
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".