The Geochemistry Of Glacier Snow And Melt: The Oregon Cascades And The Taylor Valley, Antarctica
Bibliographic record
Abstract
3.9Chemical variability over short distances (<1 km) in Taylor Valley snow...41 3.10 Sources of spatial variation to chemistry in Taylor Valley snow..42 3.11 Summary and conclusions.45 3.12 Acknowledgements45 4. Controls of Antarctic dry valley glacier melt stream geochemistry at daily, seasonal, and interannual time scales..59 4.1 Abstract..59 4.2 Glacier melt hydrochemistry..60 4.3 Site description...62 4.4 Taylor Valley proglacial streams: setting, hydrology, and eolian processes.....63 4.5 Methods..66 4.6 Results68 4.6.1 Hydrology of sampling years..68 4.6.2Wind conditions..69 4.6.3Geochemical results...69 4.7 The geochemical evolution of trace and minor elements in Taylor Valley snow, supra, and proglacial streams..74 4.7.1 Surface snow and its relation to ablation ice and supraglacial streams.74 4.7.2Environmentally available elements in snow compared with supraglacial streams.74 4.7.3Eolian influences on supra and proglacial stream chemistry..75 4.7.4Supraglacial streams and their relation with proglacial streams.75 4.8 Temporal variations in Taylor Valley stream geochemistry..79 4.8.1 Seasonal and interannual variations in proglacial element concentrations..79 4.8.2Comparison with 1982-1983 proglacial stream element concentrations..81 4.8.3Diel geochemical cycling in proglacial streams..82 4.9 Comparison of Antarctic dissolved Cu, Fe, Mn, V, and DOC concentrations with Arctic and alpine streams..86 4.10 Summary and conclusions.88 4.11 Acknowledgments.89 5. Conclusions114 APPENDIX A SF ICP-MS analytical techniques..116 APPENDIX B Canada Glacier January 2006 snowpit elemental (nM) and major ion (M) concentrations with detection limits (D.L)..119 APPENDIX C Canada Glacier December 2006 snowpit elemental (nM) and major ion (M) concentrations with detection limits (D.L)..122 APPENDIX D Commonwealth Glacier December 2006 snowpit elemental (nM) and major ion (M) concentrations with detection limits (D.L)...126
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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 | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.001 |
| 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".