{"id":"W1986544333","doi":"10.1007/s11806-007-0047-7","title":"Support vector machines for cloud detection over ice-snow areas","year":2007,"lang":"en","type":"article","venue":"Geo-spatial Information Science","topic":"Atmospheric aerosols and clouds","field":"Environmental Science","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"","keywords":"Snow; Cirrus; Remote sensing; Cloud computing; Support vector machine; Albedo (alchemy); Meteorology; Environmental science; Geology; Computer science; Artificial intelligence; Geography","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.001290688,0.0001501141,0.0001164225,0.00001297089,0.0005915576,0.0001524626,0.000384188,0.00007495695,0.001522205],"category_scores_gemma":[0.0002586331,0.0001297374,0.00006364336,0.0006793857,0.0003976729,0.002737951,0.0001615344,0.00009732899,0.0005958662],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000254649,"about_ca_system_score_gemma":0.00005074794,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00200276,"about_ca_topic_score_gemma":0.001061106,"domain_scores_codex":[0.9981145,0.000008221775,0.0004160337,0.0002245307,0.0007325686,0.0005041554],"domain_scores_gemma":[0.9992008,0.0000638054,0.0002346782,0.0002458282,0.0000621094,0.0001928453],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.0002298323,0.00007627071,0.03190333,0.00002401091,0.000007589888,0.000001436668,0.004073998,0.002814784,0.02592851,0.0008248382,0.00322439,0.930891],"study_design_scores_gemma":[0.0007937588,0.0003581085,0.7549611,0.000009537426,0.00001422279,0.00001911717,0.0003855884,0.09660983,0.03133208,0.0004598851,0.1145631,0.0004937127],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.49268,0.000001717257,0.4928276,0.0001087515,0.0009278476,0.0003510368,0.00001086418,0.00007153911,0.0130207],"genre_scores_gemma":[0.9967067,0.000002503505,0.002035489,0.000770497,0.0001605307,0.00002495096,0.00001415282,0.000006298881,0.0002788396],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9303973,"threshold_uncertainty_score":0.9993905,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007398340425338822,"score_gpt":0.2447272108509642,"score_spread":0.2373288704256254,"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."}}