{"id":"W2025400581","doi":"10.1080/01431161003801302","title":"Large-scale leaf area index inversion algorithms from high-resolution airborne imagery","year":2011,"lang":"en","type":"article","venue":"International Journal of Remote Sensing","topic":"Remote Sensing in Agriculture","field":"Environmental Science","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Leaf area index; Remote sensing; Normalized Difference Vegetation Index; Mathematics; Mean squared error; Correlation coefficient; Inversion (geology); Principal component analysis; Vegetation (pathology); Image resolution; Scale (ratio); Statistics; Geography; Geology; Cartography; Optics; Physics","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003607543,0.0002010973,0.0002377798,0.0001317846,0.0001002327,0.00006607628,0.0003928983,0.0001514026,0.0004022641],"category_scores_gemma":[0.0001120559,0.00016428,0.0001780672,0.0001788792,0.0001288344,0.0005390118,0.0002371373,0.0004450269,0.0001555498],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005172137,"about_ca_system_score_gemma":0.00002330701,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002301645,"about_ca_topic_score_gemma":0.0003366539,"domain_scores_codex":[0.9977958,0.00009670849,0.0005194362,0.0002741677,0.001040222,0.0002737187],"domain_scores_gemma":[0.9987967,0.00005939952,0.000590394,0.0001937404,0.0002043137,0.0001554713],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0008140162,0.0003238294,0.009231483,0.000005873352,0.0004653472,0.002691253,0.008922781,0.006064374,0.1188635,0.00001123139,0.02443944,0.8281668],"study_design_scores_gemma":[0.003178535,0.0002222039,0.3875964,0.0007024187,0.0001501034,0.002151852,0.002076866,0.5219176,0.05894703,0.008496252,0.01367954,0.0008811859],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8281757,0.00004029695,0.1652245,0.000652678,0.002426014,0.000063792,0.00001011502,0.00003246643,0.003374525],"genre_scores_gemma":[0.8057952,0.00004791234,0.1929995,0.0003970393,0.000565611,3.624825e-9,0.00001431522,0.00001883323,0.0001615692],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8272856,"threshold_uncertainty_score":0.6699144,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01348569055366879,"score_gpt":0.2168321540268615,"score_spread":0.2033464634731927,"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."}}