{"id":"W4206666583","doi":"10.1007/s10980-021-01384-7","title":"Extending morphological pattern segmentation to 3D voxels","year":2022,"lang":"en","type":"article","venue":"Landscape Ecology","topic":"Remote Sensing and LiDAR Applications","field":"Environmental Science","cited_by":2,"is_retracted":false,"has_abstract":false,"ca_institutions":"York University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Voxel; Adjacency list; Parsing; Computer science; Segmentation; Skeletonization; Artificial intelligence; Pattern recognition (psychology); Raster graphics; Theoretical computer science; Algorithm","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":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0001629051,0.00006350275,0.00008223056,0.00003046572,0.0002832925,0.00001070038,0.0001363234,0.00002730981,0.0146345],"category_scores_gemma":[0.00001111095,0.00006034906,0.00002327071,0.0001702703,0.00002426488,0.00002433832,0.0002538367,0.0001077648,0.001260415],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001017488,"about_ca_system_score_gemma":0.000003560233,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006416856,"about_ca_topic_score_gemma":0.00009597127,"domain_scores_codex":[0.9992396,0.00008253961,0.0001107423,0.0002477944,0.0001213539,0.0001980153],"domain_scores_gemma":[0.9997005,0.00004855483,0.00003600198,0.0001500948,0.00000204384,0.00006275537],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.000032564,0.0002894592,0.6088464,0.000002681467,0.00001725354,0.0001147878,0.001520067,0.03772411,0.05869542,0.00007162063,0.0938711,0.1988145],"study_design_scores_gemma":[0.0004418016,0.0003776668,0.8695785,8.402488e-7,0.00001537317,0.0002541666,0.000535385,0.008831947,0.0006097126,0.0003582272,0.1187323,0.0002640772],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9810297,0.000003877377,0.003338457,0.00133669,0.0002239959,0.0001881884,0.000007553956,0.00005347382,0.01381808],"genre_scores_gemma":[0.9949655,0.00000146354,0.001998857,0.001893714,0.00004155735,0.00003781313,0.00002943193,0.000007602026,0.001024053],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2607321,"threshold_uncertainty_score":0.9995172,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01007129712061284,"score_gpt":0.2407156687673533,"score_spread":0.2306443716467405,"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."}}