{"id":"W2167924377","doi":"10.5194/isprsarchives-xl-7-w2-155-2013","title":"Line-based Classification of Terrestrial Laser Scanning Data using Conditional Random Field","year":2013,"lang":"en","type":"article","venue":"The international archives of the photogrammetry, remote sensing and spatial information sciences/International archives of the photogrammetry, remote sensing and spatial information sciences","topic":"Remote Sensing and LiDAR Applications","field":"Environmental Science","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University","funders":"Ministry of Land, Infrastructure and Transport","keywords":"Conditional random field; Artificial intelligence; Classifier (UML); Pattern recognition (psychology); Computer science; Generative model; Mixture model; Inference; Ambiguity; Point cloud; Random forest; Generative grammar","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":[0.001315755,0.0006129707,0.000671322,0.003485335,0.0003887029,0.0006301029,0.00116943,0.0007340148,0.001445847],"category_scores_gemma":[0.001965719,0.000235304,0.0009731826,0.002098206,0.0004391104,0.0009129242,0.0005502839,0.0006604377,0.0009006684],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008306207,"about_ca_system_score_gemma":0.0005973252,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01179114,"about_ca_topic_score_gemma":0.01559664,"domain_scores_codex":[0.9990733,0.000187694,0.00004408813,0.000341631,0.0002391191,0.0001141996],"domain_scores_gemma":[0.9981149,0.0006886417,0.0002417728,0.0002598376,0.0006218289,0.00007305303],"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":[0.000371492,0.0003257642,0.03285005,0.000122377,0.0001414393,0.0001964408,0.0001390875,0.3529571,0.02129821,0.001909519,0.006731025,0.5829575],"study_design_scores_gemma":[0.000004172774,0.00002186915,0.004558919,0.00000598313,0.000007576967,0.00002777196,0.00002175454,0.9918802,0.002542536,0.0005633645,0.0003537617,0.00001206668],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.177118,0.0002089674,0.8144155,0.000142602,0.00005149971,0.000135738,0.00126438,0.00536162,0.001301709],"genre_scores_gemma":[0.8113181,0.00008149588,0.1834335,0.00007055511,0.00004185709,0.0001293216,0.003524773,0.0001413903,0.001258917],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01179114,"threshold_uncertainty_score":0.02344501,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03341842548066817,"score_gpt":0.279445708304905,"score_spread":0.2460272828242368,"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."}}