{"id":"W6893665305","doi":"10.5281/zenodo.4689376","title":"Latreillia Roux 1830","year":2003,"lang":"en","type":"article","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Fisheries and Oceans Canada","funders":"","keywords":"Seta; Genus; Type species; Type (biology); Larva","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":[0.0001752056,0.0007226352,0.0005160347,0.002534367,0.001818024,0.0005595973,0.000920561,0.0007054214,0.02045792],"category_scores_gemma":[0.0004390849,0.0002431454,0.0001896348,0.0006533263,0.0005401243,0.00143133,0.000959187,0.0006126655,0.004883572],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007046406,"about_ca_system_score_gemma":0.0003215024,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006106787,"about_ca_topic_score_gemma":0.01134192,"domain_scores_codex":[0.9997017,0.00004543939,0.00001997164,0.00008378844,0.00009575923,0.00005324282],"domain_scores_gemma":[0.9998668,0.00001915843,0.00004544577,0.0000100171,0.00004299737,0.00001551399],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.0004055242,0.0002295689,0.02827657,0.001383087,0.00009854699,0.002873995,0.002600512,0.000538799,0.03377649,0.009977394,0.05568542,0.8641541],"study_design_scores_gemma":[0.00003932118,0.0001998118,0.09391472,0.0004802557,0.0000464146,0.005455966,0.0006115613,0.0002524179,0.001354779,0.0005274012,0.8970803,0.0000369482],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.2207375,0.04762422,0.00403876,0.0009760428,0.001228959,0.0006226582,0.001857567,0.00124568,0.7216687],"genre_scores_gemma":[0.8839887,0.01033957,0.00604276,0.001087583,0.0008561939,0.0003299158,0.002374986,0.0001181299,0.0948621],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02045792,"threshold_uncertainty_score":0.06843859,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03452891714973229,"score_gpt":0.2510438647132168,"score_spread":0.2165149475634845,"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."}}