{"id":"W4398208056","doi":"10.3390/rs16111841","title":"Advancing Physically Informed Autoencoders for DTM Generation","year":2024,"lang":"en","type":"article","venue":"Remote Sensing","topic":"Remote Sensing and LiDAR Applications","field":"Environmental Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University","funders":"","keywords":"Computer science; Remote sensing; Environmental science; Geology","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007101741,0.0009106853,0.0005023271,0.0005002877,0.0002519786,0.0005745685,0.001006858,0.0009494988,0.001365338],"category_scores_gemma":[0.00255752,0.0005447221,0.0006091775,0.0004617898,0.0004466938,0.001002076,0.0009839981,0.001542833,0.0005508019],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007070889,"about_ca_system_score_gemma":0.0009038945,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00809512,"about_ca_topic_score_gemma":0.01267124,"domain_scores_codex":[0.9997882,0.00004224238,0.00001514189,0.00006542684,0.00006055219,0.00002843084],"domain_scores_gemma":[0.9993557,0.0003277464,0.00005645891,0.00008793649,0.0001474808,0.00002464508],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00004955491,0.00004607617,0.0008467708,0.00004731007,0.0000427194,0.00007112067,0.00004913101,0.8551794,0.006908279,0.003502687,0.001628543,0.1316284],"study_design_scores_gemma":[0.000001844499,0.000004257487,0.00006421601,0.000002429128,0.00000212871,0.000005962525,0.00000276886,0.9981987,0.0008200349,0.0006880102,0.0002076618,0.000001930408],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03804,0.0003179322,0.9576795,0.000271427,0.0001010067,0.00003596782,0.000155836,0.001589578,0.001808772],"genre_scores_gemma":[0.615502,0.0002585968,0.3793293,0.0004037659,0.00008034152,0.0001149341,0.0007536463,0.0002395959,0.00331782],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00809512,"threshold_uncertainty_score":0.016096,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01457682258619712,"score_gpt":0.2717164605617563,"score_spread":0.2571396379755592,"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."}}