{"id":"W3002497963","doi":"10.34133/2020/5801869","title":"Latent Space Phenotyping: Automatic Image-Based Phenotyping for Treatment Studies","year":2020,"lang":"en","type":"article","venue":"Plant Phenomics","topic":"Genetic Mapping and Diversity in Plants and Animals","field":"Biochemistry, Genetics and Molecular Biology","cited_by":48,"is_retracted":false,"has_abstract":true,"ca_institutions":"Agriculture and Agri-Food Canada; University of Calgary; University of Saskatchewan","funders":"Natural Sciences and Engineering Research Council of Canada; Canada First Research Excellence Fund","keywords":"Pipeline (software); Computer science; Artificial intelligence; Context (archaeology); Image processing; Pattern recognition (psychology); Association mapping; Biology; Image (mathematics); Genetics","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":[0.001108918,0.0007708222,0.0005902796,0.001188727,0.0003234905,0.0007268976,0.0008490838,0.0007465578,0.003368229],"category_scores_gemma":[0.002407261,0.0003764612,0.0007515763,0.001083427,0.0005564147,0.000605595,0.001035635,0.001030737,0.001182552],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003888923,"about_ca_system_score_gemma":0.0005273327,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001803101,"about_ca_topic_score_gemma":0.00284136,"domain_scores_codex":[0.9994832,0.000218667,0.00001880754,0.0001299458,0.0001117535,0.00003750811],"domain_scores_gemma":[0.9989686,0.0004518034,0.000142271,0.0002655421,0.0001207244,0.00005102708],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0007359859,0.0003105505,0.01140493,0.0004208018,0.0001810563,0.0002288456,0.0002926165,0.0997965,0.2490327,0.008980615,0.01341001,0.6152053],"study_design_scores_gemma":[0.00006977419,0.00009318333,0.01129512,0.00002412355,0.00003183932,0.0002401098,0.00008635737,0.9125875,0.04936434,0.01630255,0.009838644,0.00006652079],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01206231,0.00008270858,0.982752,0.0001337094,0.00001826991,0.00006060792,0.0009367924,0.003520499,0.0004331029],"genre_scores_gemma":[0.1139847,0.0001687847,0.8816484,0.0001075709,0.00003018418,0.0003053025,0.00227023,0.0007768592,0.0007079828],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003368229,"threshold_uncertainty_score":0.01126784,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06733949282130254,"score_gpt":0.2696115937878,"score_spread":0.2022721009664975,"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."}}