{"id":"W1990889196","doi":"10.1109/icdcsw.2013.10","title":"A Prototype Wireless Sensor Network for Precision Agriculture","year":2013,"lang":"en","type":"article","venue":"","topic":"Energy Efficient Wireless Sensor Networks","field":"Computer Science","cited_by":19,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Victoria","funders":"","keywords":"Wireless sensor network; Computer science; Embedded system; Precision agriculture; Wireless; Software; Data acquisition; Data collection; Soil moisture sensor; Real-time computing; Water content; Computer network; Engineering; Agriculture; Telecommunications; Operating system","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003919439,0.0005296899,0.0004183677,0.0003428683,0.0003889794,0.0003714663,0.001302995,0.0006077115,0.006659206],"category_scores_gemma":[0.0005793496,0.0001742061,0.0002289035,0.0003660188,0.000161312,0.0009330426,0.0004066243,0.0004690404,0.001877452],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003194082,"about_ca_system_score_gemma":0.0006080215,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009135451,"about_ca_topic_score_gemma":0.001292035,"domain_scores_codex":[0.999651,0.00005551947,0.00001505063,0.00008089831,0.0001603855,0.00003718752],"domain_scores_gemma":[0.9997202,0.00003903737,0.00001655895,0.00005057317,0.0001329386,0.00004082032],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0009341936,0.0005587629,0.004379646,0.001046328,0.00008291846,0.001004799,0.0003240469,0.02864964,0.4685229,0.005845616,0.04665222,0.4419989],"study_design_scores_gemma":[0.0007642196,0.005586593,0.01329113,0.0001342461,0.0001924427,0.002263963,0.0003166968,0.2782177,0.2980725,0.004897456,0.3960699,0.0001932686],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2154601,0.001301072,0.699456,0.001893961,0.001503816,0.001907398,0.002353765,0.02594225,0.05018156],"genre_scores_gemma":[0.5621657,0.0006953506,0.3930911,0.0005691566,0.00009665282,0.001363041,0.003172878,0.0004415844,0.0384045],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006659206,"threshold_uncertainty_score":0.0222773,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009438997902088532,"score_gpt":0.2155102006057911,"score_spread":0.2060712027037026,"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."}}