Bibliographic record
Abstract
Jellyfish have been drifting around the planet for more than 500 million years, yet relatively little is known about their lifestyles. Now, scientists at the University of British Columbia’s Institute for the Oceans and Fisheries are using biochemical tools to unravel a key part of the jellyfish mystery: their diet ( J. Exp. Mar. Biol. Ecol. 2021, DOI: 10.1016/j.jembe.2021.151631 ). Scientists frequently use the unique stable isotope and fatty acid signatures present in living things to investigate what predators eat. To calibrate these values for jellyfish, the team, led by Jessica Schaub, performed controlled feeding experiments at the Vancouver Aquarium. Docile moon jellies were fed frozen brine shrimp, which they unexpectedly rejected, and live krill. Voracious Japanese sea nettle jellies were fed frozen brine shrimp and the moon jellies. After analyzing the isotope and fatty acid signatures, the researchers were surprised to find that jellyfish appear to biosynthesize their own
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 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 | 0.000 |
| 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) | 0.013 | 0.003 |
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; both teacher heads agree on what is shown here.
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".