{"id":"W4316673177","doi":"10.18280/ria.360611","title":"Shrimp Body Weight Estimation in Aquaculture Ponds Using Morphometric Features Based on Underwater Image Analysis and Machine Learning Approach","year":2022,"lang":"en","type":"article","venue":"Revue d intelligence artificielle","topic":"Aquatic life and conservation","field":"Agricultural and Biological Sciences","cited_by":21,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Universitas Diponegoro","keywords":"Shrimp; Underwater; Grayscale; Artificial intelligence; Computer science; Computer vision; Image (mathematics); Mathematics; Pattern recognition (psychology); Statistics; Fishery; Biology; Geology; Oceanography","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004506889,0.00013346,0.0001913719,0.0001725771,0.0003869503,0.00008221812,0.0001508662,0.00005258605,0.0005010549],"category_scores_gemma":[0.00007182173,0.00006545015,0.00008464151,0.002804089,0.00003322147,0.0001125413,0.00006584876,0.0002693115,0.000008742158],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006762017,"about_ca_system_score_gemma":0.000007599549,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006549655,"about_ca_topic_score_gemma":0.00009230507,"domain_scores_codex":[0.9987866,0.0001796245,0.0002602071,0.0003472413,0.000225739,0.0002005914],"domain_scores_gemma":[0.9995494,0.0001883072,0.0001119935,0.00007270595,0.00003079219,0.00004676262],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001021615,0.0004421044,0.04709955,0.00002741698,0.00005747715,0.00001538659,0.000814083,0.7760839,0.1429839,0.0003215694,0.00005056778,0.03200194],"study_design_scores_gemma":[0.00003315441,0.0001399031,0.01317563,0.000007206719,0.00005255241,0.000005368927,0.0007849978,0.9806238,0.004630789,0.0001980191,0.0002004527,0.0001480824],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.973121,0.0001421099,0.02486018,0.0007738525,0.00002990976,0.0002072341,0.00001523692,0.00003699039,0.0008135129],"genre_scores_gemma":[0.997641,0.00001101855,0.001280227,0.0001939109,0.00002354033,0.00002271663,0.0002779452,0.000001712909,0.0005478964],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.20454,"threshold_uncertainty_score":0.54862,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.039449651368119,"score_gpt":0.2487457600023563,"score_spread":0.2092961086342374,"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."}}