{"id":"W4210315479","doi":"10.32920/19071638.v1","title":"Sustainable Development in Agriculture and Information & Communication Technology Sensor Networks","year":2022,"lang":"en","type":"preprint","venue":"","topic":"Greenhouse Technology and Climate Control","field":"Agricultural and Biological Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Agriculture; Productivity; Irrigation; Agricultural engineering; Yield (engineering); Agricultural productivity; Fertilizer; Agricultural science; Sustainable development; Environmental science; Crop productivity; Agricultural economics; Information and Communications Technology; Business; Agronomy; Computer science; Economics; Geography; Engineering; Ecology; Economic growth; Biology","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":[],"consensus_categories":[],"category_scores_codex":[0.000338293,0.0001857449,0.0002377482,0.00007898037,0.0003396838,0.00006931017,0.000451539,0.0006671701,0.0003531572],"category_scores_gemma":[0.00004595226,0.00007675655,0.00003019869,0.0004593332,0.00006805649,0.0001897907,0.001788943,0.0009541293,0.00001150083],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001156445,"about_ca_system_score_gemma":0.00001562793,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003900823,"about_ca_topic_score_gemma":0.003734994,"domain_scores_codex":[0.9989466,0.00007036277,0.0003380947,0.0002272362,0.0001049815,0.0003127272],"domain_scores_gemma":[0.9994988,0.000065587,0.0001756484,0.0001371664,0.00009235778,0.00003037004],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0003477549,0.0009288814,0.3336309,0.0005637842,0.0003154239,0.00008205327,0.003304122,0.01089138,0.003199746,0.1724653,0.01108574,0.4631849],"study_design_scores_gemma":[0.0004904226,0.00008785146,0.2390371,0.00007580734,0.00002595474,0.00003434725,0.01826121,0.0003963308,0.0001215652,0.005331464,0.7353223,0.0008156743],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9900249,0.000992373,0.00003564497,0.005860685,0.00003907466,0.0006698522,0.000007540907,0.0003844074,0.001985555],"genre_scores_gemma":[0.9959676,0.0008560523,0.0006862524,0.0002806519,0.00001263557,0.0003842024,0.0008077558,8.2658e-7,0.001004015],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7242366,"threshold_uncertainty_score":0.5145827,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00882209876038455,"score_gpt":0.2011110869200996,"score_spread":0.192288988159715,"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."}}