{"id":"W3010189188","doi":"10.3390/s20051449","title":"A Cost-Effective and Portable Optical Sensor System to Estimate Leaf Nitrogen and Water Contents in Crops","year":2020,"lang":"en","type":"article","venue":"Sensors","topic":"Leaf Properties and Growth Measurement","field":"Agricultural and Biological Sciences","cited_by":16,"is_retracted":false,"has_abstract":true,"ca_institutions":"Agriculture and Agri-Food Canada; University of Saskatchewan","funders":"","keywords":"Canola; Multispectral image; Greenhouse; Environmental science; Agronomy; Reflectivity; Correlation coefficient; Partial least squares regression; Remote sensing; Mathematics; Statistics; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002455621,0.0004632324,0.0003136726,0.0003343334,0.0001779724,0.0002314844,0.0006334797,0.000521875,0.0006130236],"category_scores_gemma":[0.0002348891,0.0001942769,0.0002389353,0.0003880385,0.0001427345,0.0005335484,0.0002703446,0.0002770099,0.0002785205],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002474303,"about_ca_system_score_gemma":0.0002308782,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008907923,"about_ca_topic_score_gemma":0.002248271,"domain_scores_codex":[0.999793,0.00002178869,0.000006956311,0.00006300913,0.000103384,0.00001198043],"domain_scores_gemma":[0.9998896,0.00002167002,0.00002476534,0.00001640893,0.00004151406,0.000006076588],"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":[0.00009852024,0.00008476234,0.004364271,0.0002567987,0.00002899931,0.00009153089,0.00004444621,0.002126328,0.8716599,0.0002707689,0.0008686241,0.120105],"study_design_scores_gemma":[0.00007572001,0.001182605,0.05357052,0.00004994603,0.0001643743,0.001388159,0.0001669052,0.1403709,0.7870086,0.0006291485,0.01526725,0.0001257888],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4263997,0.002392051,0.5644025,0.0002803113,0.0001854346,0.000253996,0.0006359841,0.001793967,0.003656065],"genre_scores_gemma":[0.7521186,0.0008357,0.2418364,0.0002126959,0.00005491859,0.0001829257,0.000433713,0.00003423835,0.004290721],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0008907923,"threshold_uncertainty_score":0.002050817,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04525640549590444,"score_gpt":0.2423411299516134,"score_spread":0.197084724455709,"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."}}