{"id":"W2091807821","doi":"10.1111/j.1752-1688.2007.00161.x","title":"Bottomfast Ice Mapping and the Measurement of Ice Thickness on Tundra Lakes Using C‐Band Synthetic Aperture Radar Remote Sensing<sup>1</sup>","year":2008,"lang":"en","type":"article","venue":"JAWRA Journal of the American Water Resources Association","topic":"Arctic and Antarctic ice dynamics","field":"Earth and Planetary Sciences","cited_by":43,"is_retracted":false,"has_abstract":true,"ca_institutions":"Geological Survey of Canada; Fisheries and Oceans Canada","funders":"Canadian Space Agency","keywords":"Synthetic aperture radar; Bathymetry; Remote sensing; Overwintering; Environmental science; Tundra; Oceanography; Habitat; Geology; Shelf ice; Arctic; Hydrology (agriculture); Sea ice; Ice shelf; Cryosphere; Ecology","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001972626,0.0001585951,0.0004033939,0.00008667755,0.000564565,0.00005667127,0.0002595236,0.00005086576,0.00001370259],"category_scores_gemma":[0.0005143736,0.00007491218,0.0001646746,0.0002088847,0.0004071674,0.0001268493,0.00002512115,0.0003897129,0.000002831564],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007575064,"about_ca_system_score_gemma":0.00004263812,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001700484,"about_ca_topic_score_gemma":0.00006622692,"domain_scores_codex":[0.9976243,0.0005288376,0.0004454961,0.0001380277,0.0009929897,0.0002703737],"domain_scores_gemma":[0.997663,0.0007536914,0.001131293,0.0001718413,0.0002189345,0.00006128486],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.006266013,0.000176121,0.4109975,0.000306575,0.003281397,0.0001361038,0.259394,0.2062097,0.01463225,0.00001913077,0.0006153625,0.09796584],"study_design_scores_gemma":[0.005091576,0.0008778396,0.2354932,0.001672746,0.001237298,0.002111121,0.04519837,0.6847737,0.001496133,0.001333477,0.01968436,0.001030197],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9941698,0.0001353432,0.0005499268,0.004583822,0.0001070403,0.000114219,0.000009105986,0.000006038369,0.0003246793],"genre_scores_gemma":[0.9980128,0.0001798013,0.0006888869,0.0008487126,0.0001794928,2.859079e-8,0.000001473682,0.000007280883,0.00008151776],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.478564,"threshold_uncertainty_score":0.4342234,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01367377532495456,"score_gpt":0.1912118338858335,"score_spread":0.177538058560879,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}