{"id":"W4323318403","doi":"10.3390/agriculture13030615","title":"Automated Visual Identification of Foliage Chlorosis in Lettuce Grown in Aquaponic Systems","year":2023,"lang":"en","type":"article","venue":"Agriculture","topic":"Innovations in Aquaponics and Hydroponics Systems","field":"Agricultural and Biological Sciences","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada; University of Alberta","keywords":"Chlorosis; RGB color model; Artificial intelligence; Identification (biology); Hyperspectral imaging; Precision agriculture; Aquaponics; Computer science; Computer vision; Environmental science; Agricultural engineering; Mathematics; Remote sensing; Agronomy; Agriculture; Engineering; Geography; Biology; Botany","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0007251631,0.0001489377,0.0002692968,0.00009310772,0.00008373883,0.00007000655,0.0002771094,0.0002081452,0.00001765262],"category_scores_gemma":[0.00006415516,0.00005983547,0.00006753402,0.003711374,0.00002703838,0.0001213929,0.00005704891,0.0001668529,0.0000995854],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008632723,"about_ca_system_score_gemma":0.00001134408,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001408448,"about_ca_topic_score_gemma":0.001698351,"domain_scores_codex":[0.9982692,0.0001075463,0.0007151535,0.0003069296,0.0002867935,0.0003143874],"domain_scores_gemma":[0.9994105,0.0001074976,0.0002504284,0.00007604992,0.0001242371,0.00003127372],"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":[0.000009593989,0.0001500865,0.007687322,0.00005787003,0.00001257732,0.00001332521,0.0002786444,0.00107995,0.9755157,0.01117672,0.003374843,0.0006433153],"study_design_scores_gemma":[0.0004298675,0.0001359442,0.9418234,0.0002689017,0.00001107735,0.00001361606,0.002281773,0.03890096,0.01088985,0.0005037287,0.004298174,0.00044277],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9977621,0.0002180156,0.000001153545,0.0005923061,0.0003566306,0.000404232,0.00004456351,0.000218951,0.0004020334],"genre_scores_gemma":[0.9989769,0.00008030017,0.000003477643,0.00002108089,0.0001088368,0.00008323002,0.0003319876,0.000001561529,0.0003925916],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9646259,"threshold_uncertainty_score":0.244002,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01309572371432317,"score_gpt":0.2482444764873458,"score_spread":0.2351487527730227,"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."}}