{"id":"W4398204286","doi":"10.4000/w6ly","title":"Beyond environmental monitoring: Are automatic time-lapse cameras efficient tools for temperature measurement in remote regions?","year":2023,"lang":"en","type":"article","venue":"Géomorphologie relief processus environnement","topic":"Cryospheric studies and observations","field":"Earth and Planetary Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université Laval; Center for Northern Studies","funders":"Natural Sciences and Engineering Research Council of Canada; Institut Polaire Français Paul Emile Victor; Agence Nationale de la Recherche; Labex DRIIHM","keywords":"Snow; Lapse rate; Remote sensing; Environmental science; Meteorology; Instrumentation (computer programming); Sky; Computer science; Geology; Geography","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0007292482,0.0003449787,0.0003985726,0.0001049641,0.0005264668,0.00009812139,0.0003574105,0.0001494175,0.0004306316],"category_scores_gemma":[0.0003285648,0.0002998401,0.0001155299,0.0005114862,0.0001534574,0.0001225601,0.00007758656,0.0002197992,0.0005363692],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001640692,"about_ca_system_score_gemma":0.00006876882,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003778894,"about_ca_topic_score_gemma":0.0003556676,"domain_scores_codex":[0.9971709,0.00006323445,0.0005262627,0.0006775517,0.0007209716,0.0008411325],"domain_scores_gemma":[0.9989224,0.0002628603,0.0002240984,0.0003985338,0.00003395637,0.0001581583],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0003621767,0.001200748,0.414649,0.0008912611,0.0004187082,0.0005921313,0.002923799,0.2877257,0.0106849,0.0000906455,0.04252828,0.2379326],"study_design_scores_gemma":[0.001799526,0.0005415297,0.9429141,0.0001968219,0.00008011195,0.00001457756,0.003994978,0.02873467,0.0005556206,0.0003440748,0.02013808,0.0006859275],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9904104,0.004639242,0.00009480341,0.002125313,0.0005098513,0.001520569,0.0002260138,0.0002316628,0.0002421423],"genre_scores_gemma":[0.9951851,0.001457043,0.001953767,0.0001965853,0.0001704566,0.00009017289,0.0003345931,0.00001994579,0.0005922837],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5282651,"threshold_uncertainty_score":0.9999453,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04043138175648034,"score_gpt":0.2350484676637833,"score_spread":0.194617085907303,"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."}}