{"id":"W3167643339","doi":"10.20944/preprints202104.0267.v1","title":"Drone-Based Hyperspectral and Thermal Imagery for Quantifying Upland Rice Growth and Water Use Efficiency After Biochar Application","year":2021,"lang":"en","type":"preprint","venue":"Preprints.org","topic":"Remote Sensing in Agriculture","field":"Environmental Science","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Biochar; Hyperspectral imaging; Environmental science; Canopy; Evapotranspiration; Bamboo; Normalized Difference Vegetation Index; Remote sensing; Water content; Soil water; Agronomy; Leaf area index; Soil science; Chemistry; Geography; Pyrolysis; Botany; Ecology; Biology; Geology","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0003674346,0.0003674714,0.0003132111,0.0000503873,0.000166233,0.0001504869,0.0002305545,0.0002994578,0.0001183504],"category_scores_gemma":[0.00008417723,0.0002754258,0.0001127574,0.00008446087,0.0002556447,0.0002039051,0.001064083,0.0004243294,0.0001049927],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001115623,"about_ca_system_score_gemma":0.00001637156,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006208625,"about_ca_topic_score_gemma":0.00002976787,"domain_scores_codex":[0.9975826,0.00009560148,0.0003046143,0.001316796,0.0002806439,0.0004197325],"domain_scores_gemma":[0.998987,0.0001005505,0.0001317912,0.0006125763,0.00003359309,0.0001345196],"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":[0.00005642964,0.00006991878,0.2738553,0.0001502281,0.00001985484,0.000009662142,0.001051044,0.000600453,0.7240102,0.000002960866,0.000005038973,0.0001688159],"study_design_scores_gemma":[0.0002715174,0.00001070736,0.5984395,0.00006232407,0.00005949765,0.00001652401,0.00007993146,0.002257649,0.398351,0.00004103856,0.00005039137,0.0003599223],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9955534,0.0001276207,0.002471217,0.0004445611,0.000154537,0.0009645232,0.00001818996,0.00008356871,0.0001824314],"genre_scores_gemma":[0.9951679,0.00006018526,0.00426061,0.0001595194,0.00007982406,0.00006654512,0.00008586709,0.00003752514,0.00008209614],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3256593,"threshold_uncertainty_score":0.9999698,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04741379116118459,"score_gpt":0.2751417106009995,"score_spread":0.2277279194398149,"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."}}