Microbead-Beating Extraction of Polycyclic Aromatic Compounds from Seabird Plasma and Whole Blood
Bibliographic record
Abstract
Seabirds are widely regarded as an invaluable bioindicator of environmental health. Matrices including eggs and feathers have been used as non-lethal means to assess contaminant burdens. We have developed a new approach for extraction of polycyclic aromatic compounds (PACs) from seabird plasma and serum based on automated microbead-beating homogenization and extraction. Commercially available bovine serum and plasma were purposely fortified with a suite of PACs separately at three dosing levels, placed inside a custom-made stainless-steel tube containing ceramic microbeads, and subjected to an extraction process using a Precellys tissue homogenizer. Tubes were shaken forcefully in three-dimensions, facilitating high mass-transfer of PACs from the matrix into the hexane extraction solvent. The accuracy of the method ranged from 55 to 120% and limits of detection and quantitation ranged from 0.1 to 8 and 0.2 to 27 pg/μL, respectively. The method exhibited good repeatability with both inter- and intra-day repeatability < 30%. The developed method represents an effective and efficient approach to extraction of PACs from important biological matrices.
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 | 0.000 | 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) | 0.001 | 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".