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
Synopsis Species of filter-feeding invertebrates are exposed to natural oil droplets or petroleum oil droplets in water, and many species feed on these droplets. Here, we investigate oil droplet capture by benthic tunicates. We used videography, dissections, and tetramethylrhodamine isothiocyanate (TRITC) fluorescence microscopy to study the capture of oil droplets by 10 different species of tunicate. Eight of nine species fed on waste motor oil demonstrating that it is a general phenomenon. The exception was Clavelina huntsmani. Corella willmeriana fed on light crude oil based on evidence of droplets in the branchial basket, gut, and feces. These results demonstrate that tunicates can provide an entry for oils into marine food webs. A further experiment found that Styela gibbsii fed on emulsions of fish, canola, marine 10W-30, semi-synthetic 2-cycle, and waste 5W-20 oil in filtered seawater and unfiltered seawater. It showed no selectivity despite differences in chemistry, density, viscosity, and interfacial tensions. Finally, the size distribution of oil droplets captured by S. gibbsii and Ciona intestinalis were compared to the feeding trial emulsions and found to be significantly narrower, and on the smaller end of the range. This study provides some general insights into oil droplet capture by tunicates, the mechanics of droplet capture, the absence of selection based on the type of oil, and oil droplet size capture. Tunicates are some of the most ubiquitous and abundant animals in the world’s oceans and the pelagic species significantly alter global carbon cycles. Here, we show that benthic species, common on docks and wharves, ingest natural occurring and engine oils, offering a new puzzle piece in our knowledge on the bioaccumulation and trophic transfer of oils in marine food networks.
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 | 0.000 |
| 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) | 0.004 | 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 it