Untargeted Screening and Distribution of Organo-Iodine Compounds in Sediments from Lake Michigan and the Arctic Ocean
Bibliographic record
Abstract
The majority of halogenated organic compounds present in the environment remain unidentified. To address this data gap, we recently developed an untargeted method (data-independent precursor isolation and characteristic fragment; DIPIC-Frag) for identification of unknown organo-bromine compounds. In this study, the method was adapted to enable untargeted screening of natural and synthetic organo-iodine compounds (NSOICs) in sediments. A total of 4,238 NSOIC peaks were detected in sediments from Lake Michigan. Precursor ions and formulas were determined for 2,991 (71%) of the NSOIC peaks. These compounds exhibited variations in abundances (<10(3) to ∼10(7)), m/z values (206.9304-996.9474), retention times (1.0-29.7 min), and number of iodine atoms (1-4). Hierarchical cluster analysis showed that sediments in closer proximity exhibited similar profiles of NSOICs. NSOICs were screened in 10 samples of sediment from the Arctic Ocean to compare the profiles of NSOICs between freshwater and marine sediments. A total of 3,168 NSOIC peaks were detected, and profiles of NSOICs in marine sediments were clearly distinct from Lake Michigan. The coexistence of brominated and iodinated analogues indicated that some NSOICs are of natural origin. Different ratios of abundances of iodinated compounds to brominated analogues were observed and proposed as a marker to distinguish sources of NSOICs.
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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 | 0.004 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".