Perspectives on marine zooplankton lipids
Bibliographic record
Abstract
We developed new perspectives to identify important questions and to propose approaches for future research on marine food web lipids. They were related to (i) structure and function of lipids, (ii) lipid changes during critical life phases, (iii) trophic marker lipids, and (iv) potential impact of climate change. The first addresses the role of lipids in membranes, storage lipids, and buoyancy with the following key question: How are the properties of membranes and deposits affected by the various types of lipids? The second deals with the importance of various types of lipids during reproduction, development, and resting phases and addresses the role of the different storage lipids during growth and dormancy. The third relates to trophic marker lipids, which are an important tool to follow lipid and energy transfer through the food web. The central question is how can fatty acids be used to identify and quantify food web relationships? With the fourth, hypotheses are presented on effects of global warming, which may result in the reduction or change in abundance of large, lipid-rich copepods in polar oceans, thereby strongly affecting higher trophic levels. The key question is how will lipid dynamics respond to changes in ocean climate at high latitudes?
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.003 | 0.007 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".