{"id":"W3133599571","doi":"10.3390/rs13050943","title":"Hair Fescue and Sheep Sorrel Identification Using Deep Learning in Wild Blueberry Production","year":2021,"lang":"en","type":"article","venue":"Remote Sensing","topic":"Smart Agriculture and AI","field":"Agricultural and Biological Sciences","cited_by":22,"is_retracted":false,"has_abstract":true,"ca_institutions":"Lethbridge College; University of Prince Edward Island; Dalhousie University","funders":"Natural Sciences and Engineering Research Council of Canada; Dalhousie University","keywords":"Artificial intelligence; Convolutional neural network; Computer science; Sprayer; Deep learning; Pattern recognition (psychology); Agronomy; Biology","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":[0.0001946681,0.00009863258,0.0001223628,0.00001391714,0.000235623,0.00009646963,0.00003057087,0.0000833175,0.000005761316],"category_scores_gemma":[0.0001090504,0.00004501793,0.00003718143,0.0004207774,0.00002497064,0.000158988,0.00004018012,0.0001724495,0.000006244778],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003591171,"about_ca_system_score_gemma":0.000005933182,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000333016,"about_ca_topic_score_gemma":0.002151222,"domain_scores_codex":[0.9990793,0.0001007019,0.0001790431,0.0003318197,0.0001219503,0.000187131],"domain_scores_gemma":[0.9997331,0.00004197758,0.00006790474,0.00003925691,0.00007678595,0.00004098932],"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":[0.000002923801,0.000007343413,0.0008437292,0.000004979194,0.000003007699,0.00001291806,0.0001888985,0.0003923494,0.6451005,0.000003179738,0.00001171918,0.3534285],"study_design_scores_gemma":[0.0003712401,0.00007400247,0.5440767,0.0005790665,0.00007979915,0.00159737,0.008851217,0.2881876,0.1466202,0.001154831,0.007440272,0.0009675951],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9974807,0.0003698401,0.00008924782,0.001547848,0.0002469234,0.00008084948,2.387861e-7,0.00005515807,0.0001291947],"genre_scores_gemma":[0.9981035,0.00008257935,0.0008940221,0.00006698552,0.000456646,1.460547e-8,0.00002091578,0.000001172198,0.0003741869],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.543233,"threshold_uncertainty_score":0.1835778,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0164802216190601,"score_gpt":0.2153176651439725,"score_spread":0.1988374435249124,"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."}}