{"id":"W3190472318","doi":"10.1109/icc42927.2021.9500727","title":"Optimal Placement of Camera Wireless Sensors in Greenhouses","year":2021,"lang":"en","type":"article","venue":"","topic":"Greenhouse Technology and Climate Control","field":"Agricultural and Biological Sciences","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University","funders":"","keywords":"Greenhouse; Wireless sensor network; Computer science; Real-time computing; Computer vision; Tracking (education); Cover (algebra); Wireless; Artificial intelligence; Feature (linguistics); Stability (learning theory); Pixel; Engineering; Computer network; Telecommunications","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":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00008160398,0.00007507805,0.0001635955,0.000009215111,0.00003306883,0.000006199271,0.0001093158,0.00009098726,0.00117212],"category_scores_gemma":[0.00001686406,0.00002817932,0.00004715525,0.0002359386,0.00005105735,0.00003353535,0.00005871622,0.00008108764,0.0000218671],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00000937286,"about_ca_system_score_gemma":0.000006369976,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002066127,"about_ca_topic_score_gemma":0.003095863,"domain_scores_codex":[0.9993504,0.00003653942,0.0001778689,0.0001682183,0.00008883867,0.0001781129],"domain_scores_gemma":[0.9997615,0.00008499686,0.00003929556,0.00004625479,0.00004228221,0.0000257199],"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":[0.00008704767,0.0004407642,0.1715297,0.00001714476,0.0000335256,0.0001179039,0.0001164027,0.0001940602,0.7827184,0.003363962,0.0003795135,0.04100157],"study_design_scores_gemma":[0.001048215,0.000442117,0.6369355,0.00005385894,0.00002184269,0.00004056434,0.007162014,0.0008465672,0.3481713,0.0002521232,0.00460844,0.0004175104],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9970242,0.00009838892,0.00000296091,0.001380596,0.00002686851,0.00006588144,0.000009523577,0.00007150191,0.001320099],"genre_scores_gemma":[0.9993008,0.00007003362,0.00009409971,0.00012913,0.00001452679,0.000006763351,0.000007982158,4.66475e-7,0.0003761552],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4654058,"threshold_uncertainty_score":0.999741,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01429762025475199,"score_gpt":0.2142164640444871,"score_spread":0.1999188437897352,"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."}}