Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Lipids provide the densest form of energy in marine ecosystems. They are also a solvent and absorption carrier for organic contaminants and thus can be drivers of pollutant bioaccumulation. Among the lipids, certain essential fatty acids and sterols are considered to be important determinants of ecosystem health and stability. Fatty acids and sterols are also susceptible to oxidative damage leading to cytotoxicity and a decrease in membrane fluidity. The physical characteristics of biological membranes can be defended from the influence of changing temperature, pressure, or lipid peroxidation by altering the fatty acid and sterol composition of the lipid bilayer. Marine lipids are also a valuable tool to measure inputs, cycling, and loss of materials. Their heterogeneous nature makes them versatile biomarkers that are widely used in marine trophic studies, often with the help of multivariate statistics, to delineate carbon cycling and transfer of materials. Principal components analysis has a strong following as it permits data reduction and an objective interpretation of results, but several more sophisticated multivariate analyses which are more quantitative are emerging too. Integrating stable isotope and lipid data can facilitate the interpretation of both data sets and can provide a quantitative estimate of transfer across trophic levels.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.018 | 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 it