{"id":"W4404892934","doi":"10.1007/978-3-031-72913-3_4","title":"Insect Identification in the Wild: The AMI Dataset","year":2024,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Species Distribution and Climate Change","field":"Environmental Science","cited_by":10,"is_retracted":false,"has_abstract":false,"ca_institutions":"McGill University; Espace pour la vie; Université de Sherbrooke; Canadian Heritage; Mila - Quebec Artificial Intelligence Institute","funders":"Natural Environment Research Council; Sight Research UK","keywords":"Computer science; Identification (biology); Artificial intelligence; Biology; Ecology","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":[0.0005555308,0.002200334,0.001252057,0.001957881,0.0005941523,0.0009636213,0.001907037,0.002108565,0.006414866],"category_scores_gemma":[0.001208614,0.0003890045,0.001569879,0.001872985,0.0003696449,0.0008971983,0.001153351,0.00106636,0.01505711],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005091985,"about_ca_system_score_gemma":0.0006601193,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01421552,"about_ca_topic_score_gemma":0.03832244,"domain_scores_codex":[0.9994009,0.00005990525,0.00005365971,0.0001932139,0.0001808235,0.0001115163],"domain_scores_gemma":[0.9993461,0.00008706372,0.00005312368,0.000237895,0.0001668047,0.0001089817],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00076806,0.0006080254,0.01866625,0.001084571,0.0004602924,0.0005607766,0.0001059237,0.003891162,0.005986154,0.0003706246,0.8974742,0.070024],"study_design_scores_gemma":[0.0008642921,0.0007371713,0.2518554,0.000625551,0.0005401393,0.002660255,0.001267629,0.04972479,0.01705529,0.004304644,0.6699935,0.0003713842],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.04437582,0.001482414,0.002330568,0.0004771657,0.0004180847,0.0001438387,0.940803,0.005406601,0.004562466],"genre_scores_gemma":[0.01789592,0.0002074479,0.003792541,0.000142899,0.0000630085,0.0001009779,0.9760774,0.0001041473,0.00161566],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.01421552,"threshold_uncertainty_score":0.0282656,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03406355498757208,"score_gpt":0.2615347412478275,"score_spread":0.2274711862602554,"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."}}