Cryological processes implied in Arctic proglacial stream sediment dynamics using principal components analysis and regression
Bibliographic record
Abstract
Abstract In high latitudes, recent research has demonstrated that both thermo-erosion and temperature dependence influence sediment release into fluvial systems. An analysis of proglacial suspended sediment concentration (SSC) dynamics is presented for three glacierized basins: cold-based Austre Brøggerbreen (Svalbard), polythermal Midre Lovénbreen (Svalbard) and polythermal Glacier B28 (Bylot Island). The temporal variation in processes dominating SSC patterns is assessed using stepwise multivariate regression following the subdivision of the time series. Partitioning of the time series is achieved through principal components and change point analyses. The regression models use discharge and surrogate predictor variables to model SSC, while improvements are made by using air temperature and radiation terms as independent variables. Comparisons are drawn between two sets of models with contrasting subseasonal division. By interpretation of the regression model characteristics, temporal changes in physical processes are implied over the course of the time periods. Numerical analyses suggest there is a trend for changes between fluvial, glacial and periglacial factors forcing responses in SSC. Therefore, it is conjectured that glaciofluvial sediment transfer at high latitudes is influenced by periglacial processes and conditions. This has implications for the predictions of fluvial sediment loads in a changing environment, and the use of sedimentary records for environmental reconstruction.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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".