{"id":"W3187737978","doi":"10.1101/2021.08.12.456042","title":"MorphoGraphX 2.0: Providing context for biological image analysis with positional information","year":2021,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Cell Image Analysis Techniques","field":"Biochemistry, Genetics and Molecular Biology","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal; University of Calgary","funders":"","keywords":"Context (archaeology); Morphogen; Computer science; Coordinate system; Multicellular organism; Morphogenesis; Computational biology; Artificial intelligence; Biology; Gene; Genetics","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":[0.001898286,0.001553155,0.001221462,0.002655256,0.0006151646,0.002401212,0.001945829,0.0009471795,0.02545809],"category_scores_gemma":[0.004160766,0.00131211,0.001148463,0.00104851,0.001062899,0.001876337,0.003760648,0.002651469,0.007889585],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005257384,"about_ca_system_score_gemma":0.001107554,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001008104,"about_ca_topic_score_gemma":0.00180074,"domain_scores_codex":[0.9993348,0.0001127308,0.00006937937,0.0001489687,0.0002697483,0.00006449014],"domain_scores_gemma":[0.999027,0.0004703785,0.00009535435,0.0002132396,0.0001151355,0.00007886058],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.002094294,0.0002945079,0.00975924,0.004931322,0.0008654524,0.002559108,0.002564028,0.0220438,0.1954785,0.1191269,0.3456206,0.2946623],"study_design_scores_gemma":[0.0003724872,0.0001230238,0.008448214,0.0006534487,0.0001823068,0.001456468,0.000307146,0.1732811,0.1654661,0.07931565,0.57003,0.0003640092],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"software","genre_scores_codex":[0.009178929,0.0004314986,0.7333144,0.0003913651,0.0002560907,0.0002174301,0.01181553,0.240099,0.004295744],"genre_scores_gemma":[0.08180744,0.001175573,0.7845066,0.0005600514,0.0001701012,0.001818855,0.01966024,0.1052101,0.005091068],"genre_candidate":"software","genre_consensus":null,"teacher_disagreement_score":0.02545809,"threshold_uncertainty_score":0.0851658,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007692580963304808,"score_gpt":0.2209635089995394,"score_spread":0.2132709280362346,"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."}}