Toxaphene Deposition to Lake Ontario via Precipitation, 1994−1998
Bibliographic record
Abstract
Precipitation samples collected continuously at Point Petre on Lake Ontario from November 1994 through December 1998 were analyzed for total toxaphene (=sum of hexa-, hepta-, octa-, and nonachloro bornanes) and chlorobornane congeners (1997-98 only). Composite triplicate samples were collected during 4-week intervals throughoutthe 4-year study using heated wet-only samplers. These results represent the first detailed data for toxaphene in Great Lakes precipitation. Seasonal volume-weighted mean concentrations for total toxaphene in precipitation ranged from 0.25 to 1.5 ng/L. Highest concentrations were found during the four spring (March-May) periods at roughly twice the annual means. The pattern for hexathrough nona-homologues over the 4 years did not vary appreciably with average ratios (relative to hepta-) of 0.08: 1.0:1.3:0.2. The volume-weighted mean concentrations for individual chlorobornane congeners were consistent in their season pattern with maximums seen in the spring. The major chlorobornane in precipitation, B8-2229 (Parlar 44), which was present at concentrations ranging from 0.016 to 0.079 ng/L, constituted 28 and 29% of the congener sum for 1997 and 1998, respectively. Lakewide loadings of toxaphene for Lake Ontario via precipitation were estimated to be 12, 17, 12, and 13 kg/year for 1995-1998, respectively. Previous toxaphene loading estimates were calculated for the individual Great Lakes on the basis of the only concentration data available, a single precipitation estimate of 0.2 ng/L from early work in northwestern Ontario. The loading estimates in this study indicate that precipitation inputs of toxaphene are 3-4 times higher than previously reported for Lake Ontario. The 1998 estimates of Lake Ontario wet deposition flux are 50% of the estimated gas deposition flux. However, wet flux values from this study exceed the net gas-phase mass transfer of toxaphene across the air-water interface.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".