{"id":"W2949425747","doi":"10.48550/arxiv.1602.07383","title":"Automatic Moth Detection from Trap Images for Pest Management","year":2016,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Insect Pheromone Research and Control","field":"Agricultural and Biological Sciences","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Guelph","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Pipeline (software); PEST analysis; Software deployment; Trap (plumbing); Computer science; Integrated pest management; Field (mathematics); Artificial intelligence; Codling moth; Real-time computing; Software engineering; Engineering; Ecology; Biology; Lepidoptera genitalia; Mathematics","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0001352967,0.0002060449,0.0002211172,0.00002989154,0.0002056679,0.00007865197,0.0005029202,0.000174477,0.0003883844],"category_scores_gemma":[0.00001324539,0.00009097482,0.0002524875,0.0001384384,0.00005865578,0.0001084495,0.0002625672,0.0001674414,0.00007501327],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001132519,"about_ca_system_score_gemma":0.00001110336,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004312963,"about_ca_topic_score_gemma":0.0003696879,"domain_scores_codex":[0.9987269,0.00009703274,0.0001256005,0.0006119827,0.00008279665,0.0003556669],"domain_scores_gemma":[0.9993578,0.0001839636,0.0001129693,0.0001451391,0.00007337406,0.0001267482],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0008839846,0.000565298,0.001207817,0.0003162694,0.001095664,0.0003093804,0.0001491699,0.000624021,0.1495201,0.006884458,0.002297748,0.8361461],"study_design_scores_gemma":[0.006913209,0.002168293,0.1077083,0.001040964,0.001258617,0.000006270211,0.0027457,0.183396,0.06177879,0.6203097,0.008794025,0.003880039],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9913014,0.00005946153,0.005345666,0.0002052082,0.0001804668,0.0007294848,0.0003138223,0.0001570833,0.001707371],"genre_scores_gemma":[0.9976025,0.0001646481,0.00004008626,0.00003226844,0.000260053,0.00001243764,0.0000673688,0.000002099726,0.001818602],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.832266,"threshold_uncertainty_score":0.4252536,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0473172379568958,"score_gpt":0.1765363599304683,"score_spread":0.1292191219735725,"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."}}