{"id":"W6894105883","doi":"10.5281/zenodo.8437010","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":"Water Quality Monitoring Technologies","field":"Environmental Science","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":[0.0004644288,0.0004302356,0.000405477,0.0007841801,0.0006620952,0.0006652092,0.0005403002,0.001036276,0.001073722],"category_scores_gemma":[0.000952066,0.0001830717,0.0003010552,0.001241198,0.000406706,0.0004623677,0.0003909132,0.0002525137,0.00027832],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008327799,"about_ca_system_score_gemma":0.0004909105,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.04017475,"about_ca_topic_score_gemma":0.05900947,"domain_scores_codex":[0.9997308,0.00006570916,0.0000162816,0.00006738297,0.00006573542,0.00005407139],"domain_scores_gemma":[0.9994102,0.000320684,0.0000593439,0.00005153557,0.0001111964,0.00004714188],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.001787973,0.002087385,0.4947792,0.0007656225,0.0002666172,0.04063999,0.008254358,0.1358882,0.08019781,0.002237653,0.00251243,0.2305828],"study_design_scores_gemma":[0.0001227646,0.001208885,0.5650075,0.0001281846,0.0001982449,0.003561503,0.01858731,0.3697764,0.03216584,0.001323579,0.007812599,0.0001071075],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9962185,0.00005289443,0.002517467,0.0000668151,0.000003010747,0.00003358832,0.0001034387,0.00002680316,0.0009774795],"genre_scores_gemma":[0.9937932,0.00008122435,0.004623326,0.00001053557,0.000002639267,0.00001269834,0.0001297783,0.000011061,0.001335496],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.04017475,"threshold_uncertainty_score":0.07988185,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06250957034569855,"score_gpt":0.2851423240747086,"score_spread":0.22263275372901,"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."}}