{"id":"W1882610713","doi":"10.1111/cgf.12724","title":"Full 3D Plant Reconstruction via Intrusive Acquisition","year":2015,"lang":"en","type":"article","venue":"Computer Graphics Forum","topic":"Remote Sensing and LiDAR Applications","field":"Environmental Science","cited_by":42,"is_retracted":false,"has_abstract":true,"ca_institutions":"Memorial University of Newfoundland","funders":"Shenzhen Institutes of Advanced Technology Innovation Program for Excellent Young Researchers; National Natural Science Foundation of China","keywords":"Computer science; Polygon mesh; Disjoint sets; Computer vision; Process (computing); Artificial intelligence; 3D reconstruction; Computer graphics (images); Mathematics","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":[],"consensus_categories":[],"category_scores_codex":[0.0001307256,0.0001008654,0.00008934656,0.00005409323,0.0001391879,0.00003798367,0.0001259812,0.00007269264,0.00004409887],"category_scores_gemma":[0.000002765451,0.00009670582,0.00004464363,0.0002449718,0.0001472812,0.0001372424,0.0001166501,0.0001136872,0.0003877139],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006900465,"about_ca_system_score_gemma":0.000008039615,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001275845,"about_ca_topic_score_gemma":0.00008356954,"domain_scores_codex":[0.9991933,0.00003331188,0.0001470521,0.0002469903,0.0001794202,0.0001999256],"domain_scores_gemma":[0.9995136,0.00001750718,0.000069097,0.0002513498,0.00001864203,0.0001298447],"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":[0.000130486,0.0002184947,0.02338983,0.00001113064,0.00006487802,0.00002206684,0.001601241,0.003731377,0.003236493,0.01409058,0.1203128,0.8331907],"study_design_scores_gemma":[0.0009691056,0.0005469958,0.01760885,0.00004445049,0.0000389466,0.001093431,0.0001757674,0.7925619,0.00130093,0.07231794,0.1126542,0.0006874686],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3703046,0.00002347258,0.6210627,0.001211277,0.001072222,0.0002498938,0.00001118916,0.0001745303,0.005890105],"genre_scores_gemma":[0.9686016,0.00001067297,0.030208,0.0008582077,0.0002021896,0.000003149473,0.00005653678,0.0000143372,0.00004531572],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8325032,"threshold_uncertainty_score":0.4983407,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01148550688147767,"score_gpt":0.2089482376748412,"score_spread":0.1974627307933635,"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."}}