Highly branched isoprenoids: a novel tracer of diatom-based energy pathways in freshwater food webs
Bibliographic record
Abstract
In complex food webs, it is often difficult to classify all trophic interactions, especially when the number of potential energy sources and interacting species can be high. Biochemical markers (biomarkers) can help trace energy-flow pathways from basal sources up to top predators, but can suffer from poor resolution when multiple sources all produce the same biomarker (e.g. many algae produce long-chain unsaturated fatty acids). Highly branched isoprenoids (HBIs) are unique lipids produced by diatoms, which have been successfully applied as biomarkers of diatom-derived energy pathways through marine food webs. However, currently, the existence and trophic transfer of HBIs has not been explored in freshwater food webs. Here, we confirm, for the first time, the presence of two HBI isomers (IIb and IIc) across two temperate-lake food webs, from lower basal sources up to higher trophic-position consumers (predatory fishes). Lake ecosystems are facing multiple interacting threats that could influence food-web structure and function in complex ways. HBIs could provide a novel method for tracing the outcome of altered temperature, nutrient loading and water clarity on high-quality, diatom-derived energy pathways through freshwater food webs.
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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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".