{"id":"W2102580784","doi":"10.5194/isprsannals-ii-3-w1-41-2013","title":"SINGLE TREE DETECTION FROM AIRBORNE LASER SCANNING DATA USING A MARKED POINT PROCESS BASED METHOD","year":2013,"lang":"en","type":"article","venue":"ISPRS annals of the photogrammetry, remote sensing and spatial information sciences","topic":"Remote Sensing and LiDAR Applications","field":"Environmental Science","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University","funders":"Natural Sciences and Engineering Research Council of Canada; Ontario Centres of Excellence","keywords":"Process (computing); Constraint (computer-aided design); Tree (set theory); Computer science; Laser scanning; Artificial intelligence; Gradient descent; Point (geometry); Sample (material); Computer vision; Mathematics; Algorithm; Laser","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0004616987,0.0004167538,0.0005557953,0.001339935,0.0002738344,0.0006039085,0.0009677149,0.0007788482,0.0008970466],"category_scores_gemma":[0.0008763937,0.0003166086,0.0006424485,0.001024623,0.0004319909,0.0006603153,0.0006471617,0.0004692918,0.0003030387],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002491729,"about_ca_system_score_gemma":0.0005681192,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001985424,"about_ca_topic_score_gemma":0.003133595,"domain_scores_codex":[0.999626,0.0000838718,0.00001285629,0.0001138635,0.0001315721,0.0000317603],"domain_scores_gemma":[0.9995295,0.0001857466,0.00006503397,0.00007949131,0.0001089626,0.00003137492],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0002613622,0.0001674107,0.007399184,0.0001314767,0.0001473628,0.0003732887,0.0002088423,0.5410062,0.07732385,0.005282815,0.0009192798,0.3667789],"study_design_scores_gemma":[0.000004372937,0.0000219179,0.0006354764,0.000002202003,0.000006951722,0.00005075646,0.00001004306,0.995375,0.002897971,0.0008184814,0.0001688956,0.000008044859],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05929919,0.00006340694,0.9397851,0.0000347033,0.000009414798,0.00002891782,0.0000586623,0.0003851338,0.0003353646],"genre_scores_gemma":[0.627003,0.00008990416,0.3715899,0.00002647537,0.00001950093,0.00007675023,0.000230306,0.00005436648,0.0009098556],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001985424,"threshold_uncertainty_score":0.003947735,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06116858581460271,"score_gpt":0.3171704341228005,"score_spread":0.2560018483081978,"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."}}