{"id":"W2390672234","doi":"10.1117/12.2223362","title":"An improved watershed segmentation algorithm with thermal markers for multispectral image analysis","year":2016,"lang":"en","type":"article","venue":"Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE","topic":"Remote-Sensing Image Classification","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université du Québec en Outaouais; University of Ottawa","funders":"","keywords":"Multispectral image; Watershed; Image segmentation; Computer science; Artificial intelligence; Pixel; Computer vision; Segmentation; Image (mathematics); Object (grammar); Region of interest; Scale-space segmentation; Segmentation-based object categorization; Pattern recognition (psychology)","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0004750325,0.0003598728,0.0004161822,0.0001837181,0.00007528978,0.0001503463,0.000577255,0.000162402,0.00001000142],"category_scores_gemma":[0.0001388527,0.0002474614,0.0005486632,0.0004003565,0.0002109032,0.001032828,0.00003460061,0.0001541514,9.827409e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000292855,"about_ca_system_score_gemma":0.00001886582,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000008187201,"about_ca_topic_score_gemma":4.974514e-7,"domain_scores_codex":[0.9980707,2.786688e-8,0.0005960699,0.0004283221,0.0004549194,0.0004499642],"domain_scores_gemma":[0.9980663,0.00012648,0.0002562276,0.00009511715,0.001321216,0.0001346547],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000162978,0.00006574217,0.0002246187,0.0002176417,0.001494684,7.474812e-8,0.0001796529,0.0006155548,0.9876765,0.004107708,0.0003240193,0.00493081],"study_design_scores_gemma":[0.001362678,0.0002900035,0.002595059,0.00008234478,0.0004945173,0.000004536938,0.0005377044,0.514286,0.4798297,0.00008203752,0.000114921,0.0003204477],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"methods","genre_scores_codex":[0.9744,0.00001542922,0.02336876,0.0006408192,0.000138974,0.0008266928,0.0001058429,0.0002298998,0.000273516],"genre_scores_gemma":[0.4420838,0.00002224764,0.5572886,0.00002038094,0.0002365764,0.0001407672,0.00003468807,0.00009511199,0.00007778143],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.5339199,"threshold_uncertainty_score":0.9999977,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007243058220293102,"score_gpt":0.2203347145791807,"score_spread":0.2130916563588876,"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."}}