Supplementary material to "Mass balance modelling and climate sensitivity of Saskatchewan Glacier, western Canada"
Bibliographic record
Abstract
Mass balance observationsMass balance measurements is measured by the Geological Survey of Canada (GSC) since 2012 under the joint GSC-Parks Canada initiative Columbia Icefield-Water For Life.Employing the glaciological method (Cogley et al., 2011), end-of-winter mass balance observations (bw) were derived from snow depth soundings at, and between ablation stakes along the glacier centerline (Figure 1c).Snow depths were converted to snow water equivalent (SWE) using snow density measured at a network of reference snowpits dug in the accumulation zone, near the ELA and in the ablation zone, and complemented by snow cores.End-of-summer ablation (bs) was measured at a network of 13 stakes along the glacier centerline (Figure 1c).The number of bs observations varied between years due to some stakes emerging completely from the ice before field visits, or because stakes in the upper part of the glacier were, on occasion, not accessible during field visits.bw observations are more numerous because the upper glacier was accessed by helicopter at the end of winter and the surveys conducted on skis; and because additional snow soundings were made between ablation stakes.The annual mass balance (ba) was calculated by summing the winter and summer balance data (see Demuth and Horne, 2018;Ednie et al., 2017).The data obtained over these five years were used to validate the mass balance model.An independent model validation was performed by comparing the mass balance reconstructed by the model with cumulative geodetic mass changes from 1979 to 2016.Tennant and Menounos (2013) provided geodetic mass balances for the entire Columbia Icefield and main outlet glaciers for 1979-2009.Several discrepancies and shortcomings prompted us to re-calculate the geodetic mass balance: (i) the glacier outlines used in the mass balance model excluded two disconnected ice masses and moraines included in TM2013; (ii) the 1999 SRTM DEM was not bias-corrected in TM2013, resulting in a probable bias in geodetic mass change from 1999 onward; (iii) missing data were crudely interpolated in TM2013, possibly causing further bias and explaining part of the large errors found by TM2013; (iv) the 2010 WV2 DEM was used instead of the lower quality 2009 SPOT DEM, and the 2016 Pleiades DEM was used to complement the cumulative geodetic balance series.DEM processing and uncertainty analysis on topographic changes are described in the next sub-sections.2 Horizontal registration of2016 Pleiade and 2010 WordView 2 and 2016 Pleiades DEMs Tennant and Menounos (2013) ('TM2013') horizontally coregistered their DEMs to the 1986 reference DEMs using tie points between air photos.We followed the same procedure for the 2016 Pleiades and 2010 WordlView2 (WV2) DEMs.The 2016 DEM was coregistered horizontally to the reference 1986 DEM using 35 tie points between the 2016 0.5 m resolution panchromatic Pleiades image and the 1986 orthophoto.Ties points were chosen in the same stable areas identified by Tennant and Menounos (2013).Mean horizontal biases found for the 2016 Pleiades image relative to the 1986 orthophoto were 0.8 m in X and 8.2 m in Y, with a 2D RMS error of 11.8 m.An affine transformation
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.538 | 0.083 |
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".