{"id":"W7093293448","doi":"10.7910/dvn/ne0wop","title":"MERFISH 4T1 Cell-Culture Raw Unaligned Images","year":2025,"lang":"","type":"dataset","venue":"Harvard Dataverse","topic":"Cell Image Analysis Techniques","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"BC Cancer Agency","funders":"","keywords":"Pixel; Image resolution; Medical imaging; Image processing; Resolution (logic); Cancer; Field (mathematics)","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":[0.0008233383,0.003291621,0.002063564,0.00270632,0.001174056,0.002302982,0.0031358,0.002464589,0.04350679],"category_scores_gemma":[0.00253772,0.0007429619,0.001419098,0.00329686,0.0005065949,0.0012837,0.002186054,0.001692823,0.07922511],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001281349,"about_ca_system_score_gemma":0.001699652,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01298954,"about_ca_topic_score_gemma":0.03162417,"domain_scores_codex":[0.9990191,0.00007020026,0.00008097682,0.0003438534,0.000327383,0.0001583711],"domain_scores_gemma":[0.9990087,0.0001763773,0.00008442345,0.0003279842,0.0002959583,0.0001065417],"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":[0.0003136366,0.00008338202,0.002095563,0.001946693,0.0001186746,0.0001474553,0.00004692078,0.00097495,0.00647512,0.0004603307,0.9744519,0.01288533],"study_design_scores_gemma":[0.00029747,0.00009938554,0.01418954,0.0005008469,0.0001307863,0.0006266921,0.0001696842,0.003416823,0.01122506,0.00173046,0.9675056,0.0001077875],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.001073528,0.0002785632,0.0003513221,0.00006488234,0.00003957503,0.00003610058,0.9949771,0.002096709,0.001082276],"genre_scores_gemma":[0.0008069645,0.00007329757,0.0006520903,0.00002446224,0.000004361375,0.00006400448,0.997783,0.0001336443,0.0004581999],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.04350679,"threshold_uncertainty_score":0.1455446,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.004139721146118514,"score_gpt":0.2415893934876069,"score_spread":0.2374496723414884,"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."}}