{"id":"W4384264642","doi":"10.48550/arxiv.2307.05929","title":"A New Dataset and Comparative Study for Aphid Cluster Detection","year":2023,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Mosquito-borne diseases and control","field":"Medicine","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada; U.S. Department of Agriculture","keywords":"Aphid; Cluster (spacecraft); Computer science; Sorghum; PEST analysis; Component (thermodynamics); Infestation; Artificial intelligence; Pattern recognition (psychology); Biology; Ecology; Agronomy; Botany","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"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":[0.002417958,0.003096719,0.001595127,0.005899452,0.001824888,0.002262661,0.00354694,0.003242751,0.005951912],"category_scores_gemma":[0.005101657,0.0005245771,0.002553566,0.005266232,0.0009753295,0.002347038,0.002276534,0.002282958,0.007276698],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002462797,"about_ca_system_score_gemma":0.001739077,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03029587,"about_ca_topic_score_gemma":0.05381443,"domain_scores_codex":[0.9961782,0.0004817356,0.0004347201,0.001232366,0.001238899,0.000433978],"domain_scores_gemma":[0.9960948,0.0007237602,0.0003235433,0.001236168,0.001230334,0.0003914314],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.002064064,0.002807817,0.01933712,0.004514626,0.0007341175,0.001173573,0.0003074966,0.0107624,0.01863545,0.001755933,0.7503847,0.1875226],"study_design_scores_gemma":[0.001305385,0.001511737,0.1249195,0.0009247891,0.0006955462,0.006126588,0.001825383,0.1043367,0.03511987,0.003527978,0.7192276,0.0004788596],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.1637765,0.009542353,0.01441011,0.002572857,0.003149208,0.002038384,0.7711321,0.01669969,0.01667893],"genre_scores_gemma":[0.03780888,0.0007659768,0.02024315,0.0003837222,0.0001758583,0.0005242163,0.9366411,0.0003470832,0.003110028],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.03029587,"threshold_uncertainty_score":0.06023908,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1745982651616103,"score_gpt":0.2627081363662536,"score_spread":0.08810987120464328,"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."}}