{"id":"W4402265717","doi":"10.1109/tqcebt59414.2024.10545030","title":"Application of Zero-Shot Learning in Computer Vision for Biodiversity Conservation through Species Identification and Tracking","year":2024,"lang":"en","type":"article","venue":"","topic":"Smart Agriculture and AI","field":"Agricultural and Biological Sciences","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"Horizon College and Seminary","funders":"","keywords":"Identification (biology); Biodiversity; Tracking (education); Computer science; Shot (pellet); Zero (linguistics); Computer vision; Species identification; Artificial intelligence; Biodiversity conservation; Machine learning; Ecology; Biology; Evolutionary biology","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":[0.0001540171,0.00004555523,0.0000652186,0.000008658654,0.00008283441,0.00006051509,0.00003878521,0.00004161619,0.00001051021],"category_scores_gemma":[0.000008663608,0.00001733825,0.00002664286,0.0001834829,0.00002213436,0.0002194762,0.00001740353,0.00004334269,0.000004108876],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000008465709,"about_ca_system_score_gemma":0.000001305692,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001818156,"about_ca_topic_score_gemma":0.0002541144,"domain_scores_codex":[0.9995887,0.00001876527,0.0001206629,0.0001547367,0.00005881286,0.00005836302],"domain_scores_gemma":[0.9997444,0.0001571899,0.00003459445,0.00001308368,0.00004144009,0.000009294216],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.00001438794,0.00003314646,0.04664959,0.00004032507,0.00000549577,2.602278e-7,0.0003833815,0.00003580268,0.8593751,0.002859661,0.00254148,0.08806138],"study_design_scores_gemma":[0.00007474076,0.0001216612,0.9422275,0.00003282766,0.000009127678,0.000001347232,0.000359403,0.006682577,0.0160242,0.001140964,0.03324042,0.00008527069],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9831993,0.00008578077,0.01372545,0.002604524,0.0000549321,0.0001985006,0.000008194657,0.00004410342,0.00007921653],"genre_scores_gemma":[0.999315,0.00003540211,0.0002609437,0.00007333815,0.00006235205,0.000005588161,0.0001354986,1.806625e-7,0.000111732],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8955778,"threshold_uncertainty_score":0.07070333,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03748638178970348,"score_gpt":0.2462826228653463,"score_spread":0.2087962410756428,"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."}}