{"id":"W2883192846","doi":"10.3390/f9070432","title":"Detection of Coniferous Seedlings in UAV Imagery","year":2018,"lang":"en","type":"article","venue":"Forests","topic":"Remote Sensing and LiDAR Applications","field":"Environmental Science","cited_by":64,"is_retracted":false,"has_abstract":true,"ca_institutions":"Canadian Forest Service; University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada; Alberta-Pacific Forest Industries; Cenovus Energy; ConocoPhillips","keywords":"Workflow; Context (archaeology); RGB color model; Photogrammetry; Remote sensing; Seedling; Computer science; Environmental science; Sampling (signal processing); Database; Artificial intelligence; Computer vision; Biology; Geography; Agronomy","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000595392,0.0001944207,0.0001364825,0.0007868464,0.0004945757,0.000383548,0.0002956929,0.0001070862,0.0008122157],"category_scores_gemma":[0.00080133,0.0001142049,0.00009965226,0.0006383477,0.0002519058,0.0001999601,0.000228815,0.0001280448,0.0002626552],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007338011,"about_ca_system_score_gemma":0.0007358477,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.1636951,"about_ca_topic_score_gemma":0.5309624,"domain_scores_codex":[0.9996758,0.00003781934,0.00001292773,0.00007915312,0.0001315202,0.00006283163],"domain_scores_gemma":[0.9993442,0.000126834,0.0001083223,0.00004740742,0.00031964,0.00005366327],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0004765004,0.00007854931,0.656651,0.0002051585,0.00006028935,0.0004492344,0.001823809,0.004624798,0.1684809,0.0003496303,0.002161481,0.1646386],"study_design_scores_gemma":[0.000009629196,0.0001485386,0.9441642,0.00004105216,0.00003660539,0.0002905859,0.002399171,0.02003163,0.02864814,0.000146725,0.004062594,0.00002112267],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9889781,0.0001469336,0.00763973,0.00001698778,0.000008964735,0.0000811436,0.0005300273,0.0001811736,0.002417],"genre_scores_gemma":[0.9772398,0.00009544917,0.02128874,0.00001944595,0.00000176316,0.00002541737,0.0004315321,0.00001189561,0.0008859878],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1636951,"threshold_uncertainty_score":0.3254846,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007138828090991553,"score_gpt":0.2290831125494578,"score_spread":0.2219442844584662,"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."}}