Bibliographic record
Abstract
ABSTRACT. Arctic lakes can provide a long-term perspective on environmental change, including trends in long-range atmospheric transport and deposition of contaminants, inferred from studies of sediment cores. In this study, we conducted the first detailed bathymetric survey of Lake Hazen (Quttinirpaaq National Park, Ellesmere Island, Nunavut), the world’s largest lake north of 74 ˚ latitude. With these data we were able to determine optimal locations for sediment coring and to collect and analyze profundal sediment cores. A bathymetric map of Lake Hazen was developed on the basis of 362 spot soundings obtained with GPS-coupled sounding equipment placed directly on the ice combined with additional soundings from a small boat. The deepest point in the lake (81 4̊9.5 ʹ N; 70 4̊2.8 ʹ W) was found to be 267 m deep. The lake volume was estimated to be 5.14 × 1010 m3, about 10 % larger than previous estimates. Using estimates of the discharge of Lake Hazen from the Water Survey of Canada, we estimated the water retention time to be 89 years. Sediment cores were dated using 210Pb and 137Cs. Sedimentation rates at the deep point, estimated using the Constant Rate of Supply model for excess 210Pb, were relatively high (1260 g m-2yr-1) in the period 2005 – 1963 and lower (650 ± 100 g m-2yr-1) in horizons dated to 1950 – 1880. The majority of elements measured in sediment (24 of 29 consistently above detection limits), as well as organic carbon, showed less than 20 % variation in concentrations in the top 10 cm (compacted depth), which represent deposition over approximately 140 years. Geochemical characteristics of the sediment suggest that erosional inputs from annual glacial melting are the major source of essentially all elements and that anthropogenic inputs from long-range transport of toxic metals such as mercury and lead are very low.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.278 | 0.203 |
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".