{"id":"W4416273714","doi":"10.1016/j.forsciint.2025.112728","title":"Technical note: The impact of image size on bloodstain pattern analysis using machine learning","year":2025,"lang":"en","type":"article","venue":"Forensic Science International","topic":"Forensic and Genetic Research","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ontario Tech University","funders":"","keywords":"Image stitching; Image (mathematics); Pattern recognition (psychology); Feature (linguistics); Selection (genetic algorithm); Key (lock); Contextual image classification","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005997088,0.0000914005,0.0001086693,0.0001939776,0.000130004,0.00005019516,0.0005503666,0.00004729579,0.00007944786],"category_scores_gemma":[0.0008374839,0.00005797018,0.0001925366,0.0007169307,0.0008528772,0.000005386666,0.0002666756,0.0001375829,0.000002300953],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005942376,"about_ca_system_score_gemma":0.0002361217,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004728594,"about_ca_topic_score_gemma":0.00009760715,"domain_scores_codex":[0.99888,0.00003866767,0.0001773017,0.0002824285,0.0004215478,0.00020003],"domain_scores_gemma":[0.9992486,0.00006748406,0.0000747849,0.0002771276,0.0002936583,0.00003840488],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0001142693,0.00007150994,0.05774512,0.000002839582,0.0001974549,0.000002817245,0.00004408561,0.01170227,0.9089336,0.0002363586,0.0004029593,0.02054672],"study_design_scores_gemma":[0.0006061185,0.0006048465,0.167606,0.00003080876,0.00009950133,0.0000160485,0.00009189368,0.2786583,0.5509688,0.0007082915,0.0004152275,0.0001940476],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.959098,0.0000378853,0.03757462,0.0003695562,0.0001154487,0.00009351684,0.00002425405,0.000005012281,0.002681695],"genre_scores_gemma":[0.9973898,0.00001079222,0.001971208,0.00008918292,0.00005520357,0.000003375101,0.00002291388,0.000004039366,0.0004534973],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3579648,"threshold_uncertainty_score":0.3142462,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01141395939728444,"score_gpt":0.3618976834199743,"score_spread":0.3504837240226898,"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."}}