Degradation as a Loss Mechanism in the Fate of α-Hexachlorocyclohexane in Arctic Watersheds
Bibliographic record
Abstract
Water extracts of samples collected from Amituk Lake in July−August, 1994 and samples collected at Char and Meretta Lakes in July 1997 were analyzed for enantiomers and concentrations of α-HCH to estimate the extent of biodegradation in watersheds in the Canadian High Arctic. (+)/(−)-α-HCH enantiomer ratios (ERs) in three streams entering Amituk Lake ranged from racemic values of 1.01 in snow to 0.36 in meltwater. Lower ERs were promoted by warmer temperatures and increased contact with stream substrates during low streamflows, especially biologically productive substrates. Most α-HCH degradation occurred during peak runoff when ERs were 0.95−0.80, rather than later in summer when ERs reached their minimum. Approximately 7% of α-HCH in the Amituk Lake basin was enantioselectively degraded prior to entering the lake. ERs within Amituk Lake are controlled by meltwater inputs rather than within lake degradation and clearly illustrate the riverine-like nature of high arctic lakes. Differences in lake α-HCH inventory from end of summer 1993 to spring 1994 indicate that from 33 to 61% of α-HCH within the lake may have been lost via nonenantioselective microbial degradation at a rate ranging from 0.48 to 1.13 y - 1 .
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 | 0.001 |
| 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) | 0.000 | 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".