{"id":"W4412122171","doi":"10.5194/epsc-dps2025-1392","title":"Needles in a Haystack: Harnessing Machine Learning and Citizen Science to Catch Small Body Activity in Action","year":2025,"lang":"en","type":"preprint","venue":"","topic":"Big Data and Business Intelligence","field":"Business, Management and Accounting","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Haystack; Action (physics); Citizen science; Psychology; Human–computer interaction; Computer science; Biology; World Wide Web; Physics","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001018039,0.0008658065,0.0004257882,0.002296671,0.000447488,0.001331261,0.0008202439,0.001137675,0.001028303],"category_scores_gemma":[0.003080536,0.0002733133,0.0004715743,0.001112407,0.0006507573,0.002065166,0.001942471,0.0009677675,0.001225391],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006176131,"about_ca_system_score_gemma":0.0005316928,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008740854,"about_ca_topic_score_gemma":0.02236731,"domain_scores_codex":[0.9994667,0.00009781069,0.00002071374,0.000191339,0.0001327547,0.00009072915],"domain_scores_gemma":[0.9988231,0.0003142692,0.0001829633,0.0003243385,0.0002452908,0.0001099892],"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":[0.0003311533,0.0003703883,0.1702033,0.0003011441,0.0002389324,0.0006242172,0.001113899,0.03578471,0.02933736,0.00301831,0.02413809,0.7345386],"study_design_scores_gemma":[0.00003759309,0.0002933694,0.09804001,0.0002319654,0.0001125619,0.0006780808,0.001601879,0.8085692,0.03573218,0.01793003,0.03667069,0.0001024093],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7128032,0.003626456,0.2385252,0.002717783,0.0005463693,0.0003972757,0.005776823,0.01176637,0.02384047],"genre_scores_gemma":[0.8444828,0.0006473375,0.1427907,0.0008162054,0.0001736241,0.00008757102,0.00578344,0.0003819947,0.004836429],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.008740854,"threshold_uncertainty_score":0.01737994,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08517644355810108,"score_gpt":0.3327203815517822,"score_spread":0.2475439379936811,"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."}}