{"id":"W3180321043","doi":"10.1080/07038992.2021.1931786","title":"CJRS’ Special Issue on Deep Learning for Environmental Applications of Remote Sensing Data","year":2021,"lang":"en","type":"article","venue":"Canadian Journal of Remote Sensing","topic":"Remote-Sensing Image Classification","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"Computer Research Institute of Montréal; Institut National de la Recherche Scientifique; Centre For Cold Ocean Resources Engineering; Memorial University of Newfoundland","funders":"","keywords":"Geography; Remote sensing; Data science; Deep learning; Cartography; Computer science; Artificial intelligence","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":true,"about_ca":false,"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":[0.0004008995,0.0002182925,0.0003768782,0.0003410764,0.0002200944,0.00009478788,0.0002214991,0.0001417391,0.00002881722],"category_scores_gemma":[0.0004177509,0.0002653511,0.0001267021,0.0002712228,0.0001073474,0.0001809823,0.00002585146,0.0004986679,0.00002061901],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004768404,"about_ca_system_score_gemma":0.0003156994,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002277481,"about_ca_topic_score_gemma":0.003171922,"domain_scores_codex":[0.9983506,0.00007806872,0.0006366681,0.0002856539,0.0002509798,0.0003980565],"domain_scores_gemma":[0.9983028,0.0001671859,0.0003046743,0.0006646023,0.0001834862,0.0003772977],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00000899856,0.000002244011,0.000003639146,0.00004217333,0.00005972469,0.0001359901,0.0003163916,0.005745064,0.02273582,0.000002447829,0.001068088,0.9698794],"study_design_scores_gemma":[0.0003395943,0.00003412195,0.0001494117,0.0002505473,0.00008470605,0.0008445943,0.0006471885,0.5764482,0.0179496,0.0001342423,0.4028875,0.0002303079],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.02207801,0.0006219067,0.9693681,0.000571293,0.001259648,0.0002791081,0.00003983675,0.00003645544,0.00574565],"genre_scores_gemma":[0.2520039,0.0001712483,0.741399,0.0001249896,0.005470986,7.141215e-9,0.0001780782,0.0001525695,0.000499204],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9696491,"threshold_uncertainty_score":0.9999799,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02173943176397656,"score_gpt":0.233924699195979,"score_spread":0.2121852674320024,"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."}}