{"id":"W4413031033","doi":"10.1016/j.plaphe.2025.100084","title":"The Global Wheat Full Semantic Organ Segmentation (GWFSS) dataset","year":2025,"lang":"en","type":"article","venue":"Plant Phenomics","topic":"Smart Agriculture and AI","field":"Agricultural and Biological Sciences","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Saskatchewan","funders":"","keywords":"Segmentation; Computer science; Artificial intelligence; Natural language processing","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000613281,0.003439138,0.001282979,0.002431769,0.0008146134,0.001521428,0.00216882,0.002313487,0.005892672],"category_scores_gemma":[0.0009925148,0.0005283411,0.001900304,0.002142147,0.0005820975,0.0009056269,0.001364698,0.001244932,0.008621887],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001153236,"about_ca_system_score_gemma":0.000976796,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.014729,"about_ca_topic_score_gemma":0.03627632,"domain_scores_codex":[0.9993382,0.00006118008,0.00004996749,0.0002954422,0.0001522405,0.0001030217],"domain_scores_gemma":[0.9996511,0.00006477009,0.00002658719,0.0001070729,0.0001082426,0.00004226254],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.001133611,0.0007481614,0.01134169,0.003582493,0.0006356025,0.001283335,0.0002865834,0.01550578,0.03792623,0.001929461,0.7963223,0.1293048],"study_design_scores_gemma":[0.0008507693,0.0007955471,0.07902705,0.000784722,0.0006110448,0.003550587,0.001047253,0.1157738,0.0589091,0.007477629,0.7308073,0.0003651931],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.09193106,0.004298134,0.01559596,0.0007383188,0.0006520897,0.0005053793,0.8504866,0.02334251,0.0124499],"genre_scores_gemma":[0.02933125,0.0003431132,0.01111717,0.0001928582,0.00003001901,0.0001768014,0.9561764,0.0003664744,0.002265999],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.014729,"threshold_uncertainty_score":0.0292865,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009197332697088261,"score_gpt":0.2104365869731709,"score_spread":0.2012392542760826,"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."}}