Subglacial hydrology within the Amery Ice Shelf catchment
Bibliographic record
Abstract
These datasets correspond to the results from the manuscript:Characterizing subglacial hydrology within the Amery Ice Shelf catchment using numerical modelling and satellite altimetry (Wearing et al.,)Martin G. Wearing1,2, Christine F. Dow3, Daniel N. Goldberg2, Noel Gourmelen2, Anna E. Hogg4, Livia Jakob5 1ESA Centre for Earth Observation, ESRIN, Frascati, Rome, Italy 2School of Geosciences, University of Edinburgh, Edinburgh, UK 3Department of Geography and Environmental Management, University of Waterloo, Ontario, Canada 4School of Earth and Environment, University of Leeds, Leeds, UK 5Earthwave Ltd, Edinburgh, UK The datasets are:1) Subglacial melt rates. These are calculated using:- basal dissipation from numerical ice sheet inversion with the ice-flow model STREAMICE (https://mitgcm.readthedocs.io/en/latest/phys_pkgs/streamice.html) run at 2km and 5km resolution.- geothermal heat flux estimates from Shen et al., (2020) and Martos et al., (2017)- englacial temperature from Van Liefferinge & Pattyn (2013)Variables include in each file are:Position coordinates (X (m) & Y (m)) in polar stereographic projection WGS 1984Geothermal heat flux (GHF (W/m^2))Basal friction dissipation (Basal_disp (W/m^2))Vertical conduction (Vert_Cond (W/m^2))Subglacial melt rate (Melt_rate (m/yr)) 2) Subglacial hydrology. This is determined using the GlaDS model (Werder et al., 2013) with high and low channel conductivity and subglacial melt rate calculated using Shen GHF and 2km resolution basal dissipation.Model results are provided in netcdf format for high and low conductivity:Amery_subglacial_hydrology_GlaDS_high_conductivity.ncAmery_subglacial_hydrology_GlaDS_low_conductivity.ncVariables includes in each file are:Node X coordinate (m)Node Y coordinate (m)Edge X coordinate (m)Edge Y coordinate (m)Channelized discharge (m3/s)Effective pressure (Pa)Subglacial water pressure (Pa)Sheet discharge (x-direction) (m3/s)Sheet discharge (y-direction) (m3/s)sheet thickness (m)Channel cross-sectional area (m2) 3) Ice-shelf basal melting is calculated from CryoSat-2 interferometric-swath radar altimetry acquired from 2010 to 2020, using the mass conservation approach (Gourmelen et al., 2017). Melt rates and errors are provided in the file: Amery_ice_shelf_basal_melt_rate.nc 4) The outlines of active subglacial lakes detected from CryoSat-2 interferometric-swath radar altimetry acquired from 2010 to 2020, using the method of Malczyk et al. (2020) are provided as geojson files in the zipfile: Amery_subglacial_lakes.zip
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
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".