{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00007832887,0.00003544671,0.0000468389,0.00002051642,0.00003136631,0.000004580803,0.00005011375,0.00002701159,0.00007124327],"category_scores_gemma":[0.00001396603,0.00003351808,0.00001481952,0.000147909,0.0001503082,0.00004169908,0.00002562004,0.00003703316,0.000360043],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004268268,"about_ca_system_score_gemma":0.000004276415,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007669615,"about_ca_topic_score_gemma":0.004321873,"domain_scores_codex":[0.9996413,0.000009277886,0.00008999216,0.00009887792,0.00007343903,0.00008710525],"domain_scores_gemma":[0.9998069,0.0000135929,0.00003223383,0.0001216051,0.000005074003,0.00002062695],"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":[0.00003171678,0.00009193723,0.3333394,0.00000729883,0.000004087552,0.000003706311,0.001539575,0.0003144022,0.5234933,0.00005946868,0.0009502067,0.1401649],"study_design_scores_gemma":[0.00009802546,0.00004533079,0.9100469,0.000006356918,0.000001882245,0.000007153144,0.00001675995,0.002817168,0.08343201,0.001329861,0.002149171,0.0000493224],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9857503,0.000003361083,0.001530397,0.00004188349,0.00004340319,0.00005974492,5.702614e-7,0.00001528833,0.01255506],"genre_scores_gemma":[0.999015,8.069781e-7,0.0005866837,0.00002650104,0.00003349542,7.941377e-7,8.062791e-7,0.000004324302,0.0003315288],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5767075,"threshold_uncertainty_score":0.4627745,"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."}}