Supplementary material to "Mass balance modelling and climate sensitivity of Saskatchewan Glacier, western Canada"
Notice bibliographique
Résumé
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
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,004 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,002 | 0,005 |
| Études des sciences et des technologies | 0,002 | 0,000 |
| Communication savante | 0,002 | 0,001 |
| Science ouverte | 0,002 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,538 | 0,083 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».