Developing Frameworks for Studies on Sedimentary Fluxes and Budgets in Changing Cold Environments
Bibliographic record
Abstract
Developing Frameworks for Studies on Sedimentary Fluxes and Budgets in Changing Cold Environments Geomorphic processes that are responsible for the transfer of sediments and landform change are highly dependent on climate and vegetation cover. It is anticipated that climate change will have a major impact on the behaviour of Earth surface systems and that the most profound changes will occur in high-latitude and high-altitude cold environments. Collection, comparison and evaluation of data from a range of different high-latitude and high-altitude cold environments are required to permit greater understanding of sedimentary fluxes in cold environments. The focus of the I.A.G./A.I.G. SEDIBUD (Sediment Budgets in Cold Environments) Programme is the analysis of source-to-sink fluxes and sediment budgets in changing cold environments. Establishing contemporary sediment fluxes in a diversity of cold environments will form a baseline for modelling. At a minimum, baseline information from defined SEDIBUD test sites must consist of measures of mean annual precipitation, stream discharge, suspended load, conductivity/TDS and dominant catchment processes. Reports from ongoing studies on sedimentary fluxes and budgets in three selected study sites in Arctic Canada, sub-Arctic Iceland and sub-Arctic Norway are presented and discussed in the context of effects of climate change on process rates and sediment budgets in sensitive cold environments. Comparable datasets and coordinated data collection and data exchange will be of use for the individual studies at the different study sites. In addition, comparable data sets and data exchange will help to improve our understanding of existing relationships between contemporary climate and sedimentary fluxes and will enable larger-scale integrated investigations on effects of climate change in changing cold environments.
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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 | 0.043 | 0.039 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.007 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.010 | 0.012 |
| Open science | 0.010 | 0.009 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".