Kaskawulsh River headwaters: reference model ensemble-mean outputs
Bibliographic record
Abstract
This dataset contains outputs from the distributed mass-balance model originally developed by Young et al. (2021) and adapted by Robinson et al. (2024). Model outputs span 1980–2022 and include 13 different mass balance and runoff variables for the Kaskawulsh River headwaters, a highly-glacierized catchment in Southwest Yukon. These are the outputs for the “reference model”, a 100-simulation ensemble tailored to the catchment with site-specific observations of accumulation, the debris-ablation relationship, the geodetic mass balance, and transient snowlines. For more details, see: Robinson, K. M., Flowers, G. E., & Rounce, D. R. (2024). Sensitivity of modelled mass balance and runoff to representations of debris and accumulation on the Kaskawulsh Glacier, Yukon, Canada. Robinson, K. (2024). Reconstructing a multi-decadal runoff record for a highly-glacierized catchment in Yukon, Canada. MSc Thesis. https://summit.sfu.ca/item/38185 Output variables (all in units of m w.e.) include: Mass balance Glacier ice melt Snowmelt Superimposed ice melt Refreezing Net snow melt (total snowmelt minus refreezing) Accumulation Rainfall Refrozen rainfall Rain runoff (rainfall minus refrozen rain) Superimposed-ice layer thickness (‘SI’) Snow depth Potential retention mass (‘Ptau’) Three types of output files are included in this dataset: The raw netcdf file outputs from the model (NETCDF_FILES) 2 files per year per variable (the ensemble mean and ensemble standard deviation) (see Robinson et al. 2024) File dimensions are t,x,y, = (365 days, 230 gridcells x 329 grid cells). Grid Cells are 200m x 200m. Units are m w.e. day-1, except for the variables snow depth, superimposed ice, and potential retention mass, which have units of m w.e. and reflect the total depth/thickness on each day. Distributed outputs ({variable}_distributed) 1 text file per year per variable.. Each file contains the distributed annual total for a given variable (units = m w.e. a-1). Annual totals are summed over the hydrological year (Oct 1-Sept 30). (e.g. accumulation_distributed_2000.txt is the total accumulation summed over Oct 1 2000 to Sept 30 2001). File dimensions are x,y = 230 gridcells x 329 grid cells. Glacier-wide mean timeseries (timeseries_1979-2022) Files are labeled by: REF_MODEL_{variable}_{domain}_{years}.txt Variables are averaged over: Catchment-wide (domain = ‘krh’) Kaskawulsh Glacier only (domain = ‘kw’) All glacierized areas (domain = ‘allgl’) File dimensions are 43 x 366 (year x DOY). NaN replaces the Feb 29th value on a non-leap year. Each row spans one hydrological year (Oct 1-Sept 30). Units are (m w.e. day-1), except for the variables snow depth, superimposed ice, and potential retention mass, which have units of m w.e. and reflect the total depth/thickness on each day.
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 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.044 |
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; both teacher heads agree on what is shown here.
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".