{"id":"W4408187154","doi":"10.3390/drones9030192","title":"Assessment of the Maize Crop Water Stress Index (CWSI) Using Drone-Acquired Data Across Different Phenological Stages","year":2025,"lang":"en","type":"article","venue":"Drones","topic":"Remote Sensing in Agriculture","field":"Environmental Science","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"United Nations University Institute for Water, Environment, and Health","funders":"Water Research Commission","keywords":"Phenology; Drone; Water stress; Crop; Index (typography); Environmental science; Agronomy; Biology; Computer science; Botany","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":[0.0002268982,0.0003849091,0.0001940795,0.0005911993,0.0001038446,0.0002443938,0.0001460789,0.0001761425,0.000333848],"category_scores_gemma":[0.0004472655,0.00009870859,0.000244041,0.0004045356,0.00006189047,0.0003247723,0.0001528883,0.0001361923,0.0001409977],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001766747,"about_ca_system_score_gemma":0.000142838,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005417684,"about_ca_topic_score_gemma":0.009746399,"domain_scores_codex":[0.9999104,0.00001017647,0.0000048733,0.00003677829,0.00002483385,0.00001305855],"domain_scores_gemma":[0.9998409,0.00003398609,0.0000449151,0.00001286796,0.00005638287,0.00001096713],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0003686435,0.0001939405,0.496026,0.0002077085,0.0002533157,0.000217045,0.0002813766,0.08749606,0.2348391,0.0003937065,0.0009304218,0.1787925],"study_design_scores_gemma":[0.00001022182,0.0001812576,0.6620244,0.00001524681,0.0000564493,0.0001380775,0.0001573936,0.3093881,0.02677593,0.0001485665,0.001069226,0.00003519346],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9795167,0.0001242351,0.01871782,0.00001586609,0.000009414758,0.00001951941,0.0006616568,0.0002016036,0.0007330428],"genre_scores_gemma":[0.991276,0.0000596998,0.007596491,0.000006132095,0.000003282603,0.00001398965,0.0007741736,0.00001186562,0.0002582872],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.005417684,"threshold_uncertainty_score":0.01077229,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03485931620541235,"score_gpt":0.32223675911861,"score_spread":0.2873774429131977,"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."}}