Untargeted Screening and Distribution of Organo-Bromine Compounds in Sediments of Lake Michigan
Bibliographic record
Abstract
Previously unreported natural and synthetic organo-bromine compounds (NSOBCs) have been found to contribute more than 99% of total organic bromine (TOB) in environmental matrices. We recently developed a novel untargeted method (data-independent precursor isolation and characteristic fragment, DIPIC-Frag) and identified ∼2000 NSOBCs in two sediments from Lake Michigan. In this study, this method was used to investigate the distributions of these NSOBCs in 23 surficial samples and 24 segments of a sediment core from Lake Michigan. NSOBCs were detected in all 23 surficial samples and exhibited 10- to 100-fold variations in peak abundance among locations. The pattern of distributions of NSOBCs was correlated with depth of the water column (r(2) = 0.61, p < 0.001). Hierarchical cluster analysis showed that sediments in close proximity exhibited similar profiles of NSOBCs. Distributions of NSOBCs in 24 segments of a sediment core dated from 1766 to 2008 were investigated, and samples from similar depths exhibited similar profiles of NSOBCs. NSOBCs were grouped into four clusters (soft-cluster analysis) with different temporal trends of abundances. 515 and 768 of the NSOBCs were grouped into cluster 1 and cluster 3 with increasing temporal trends, especially since 1950, indicating that abundances of these compounds might have been affected by human activities.
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) | 0.000 | 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 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".