{"id":"W4409428728","doi":"10.1101/2025.04.08.647223","title":"A General Method for Detection and Segmentation of Terrestrial Arthropods in Images","year":2025,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Digital Imaging for Blood Diseases","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Segmentation; Artificial intelligence; Computer vision; Computer science; Pattern recognition (psychology); Geology","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":[0.001026217,0.001935674,0.001103778,0.002695675,0.0008142281,0.001773407,0.003398403,0.002816187,0.007044367],"category_scores_gemma":[0.001823016,0.001430696,0.002873968,0.002464779,0.001230396,0.001703787,0.002174977,0.002507319,0.01004432],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001159126,"about_ca_system_score_gemma":0.002057346,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01019446,"about_ca_topic_score_gemma":0.02215228,"domain_scores_codex":[0.9989486,0.0000777083,0.00006921295,0.0004943534,0.0003018684,0.0001083117],"domain_scores_gemma":[0.9995154,0.00007943646,0.00006669773,0.0001722169,0.000131613,0.00003457831],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002922094,0.0001363493,0.002230177,0.0008049322,0.0002930349,0.0002354137,0.0002830201,0.04669087,0.08860616,0.01116777,0.04742569,0.8018344],"study_design_scores_gemma":[0.00008227714,0.0002324566,0.005781825,0.0001801803,0.0001033284,0.001557119,0.0001105223,0.7933144,0.0693891,0.0238028,0.1053194,0.0001266441],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.002870527,0.0005280953,0.9788591,0.000146479,0.00009864289,0.0002378583,0.001707824,0.01432464,0.001226859],"genre_scores_gemma":[0.02717097,0.0006866931,0.9547334,0.0004943164,0.00007723985,0.0005575427,0.007435463,0.002039577,0.006804894],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01019446,"threshold_uncertainty_score":0.02356577,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0140187087861727,"score_gpt":0.2722552121667927,"score_spread":0.25823650338062,"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."}}