{"id":"W6931741330","doi":"10.5281/zenodo.8437009","title":"COMPUTER VISION-BASED URCHIN BIOMASS ESTIMATION: A CASE STUDY IN SOUTH AFRICA IMTA FARM","year":2023,"lang":"en","type":"article","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Prenatal Screening and Diagnostics","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Canadian Society of Intestinal Research","funders":"Horizon 2020 Framework Programme","keywords":"Biomass (ecology); Aquaculture; Livestock; Animal production; Production (economics); Yield (engineering)","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":[0.0004634731,0.0004387318,0.0003778473,0.0009184452,0.000676444,0.0007012463,0.0005768223,0.001107682,0.001069483],"category_scores_gemma":[0.001120514,0.0001931388,0.0003123739,0.001248578,0.0004240333,0.0004368323,0.0004373338,0.0002888622,0.0003057708],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007517615,"about_ca_system_score_gemma":0.0004552091,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02820625,"about_ca_topic_score_gemma":0.0408299,"domain_scores_codex":[0.9996941,0.00007574103,0.00001969701,0.00007350179,0.00007942832,0.00005766713],"domain_scores_gemma":[0.9993766,0.0003470855,0.0000653233,0.0000532515,0.0001112728,0.00004650347],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.001127353,0.001740333,0.5916542,0.0006733612,0.0002289553,0.06115543,0.0109179,0.06641482,0.06015981,0.001927565,0.002103806,0.2018965],"study_design_scores_gemma":[0.00007131007,0.001204961,0.6499851,0.0001718413,0.0001927148,0.00928711,0.02944733,0.2688649,0.03004924,0.001611361,0.008987116,0.0001270795],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9954828,0.00006941242,0.00322523,0.00008367006,0.000003496884,0.00003135766,0.0001025116,0.00002564146,0.0009759425],"genre_scores_gemma":[0.9931692,0.0001142737,0.00528741,0.00001275674,0.000003094277,0.00001193756,0.000113798,0.00001260249,0.001274969],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02820625,"threshold_uncertainty_score":0.05608416,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05169712831893332,"score_gpt":0.2969485071479144,"score_spread":0.2452513788289811,"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."}}