{"id":"W4229375534","doi":"10.1186/s13595-022-01143-x","title":"Closing the gap between phenotyping and genotyping: review of advanced, image-based phenotyping technologies in forestry","year":2022,"lang":"en","type":"article","venue":"Annals of Forest Science","topic":"Remote Sensing and LiDAR Applications","field":"Environmental Science","cited_by":77,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"Qinglan Project of Jiangsu Province of China; Jiangsu Provincial Key Research and Development Program; Jiangsu Agricultural Science and Technology Innovation Fund; Government of Jiangsu Province; Six Talent Peaks Project in Jiangsu Province; National Natural Science Foundation of China","keywords":"Bottleneck; Tree (set theory); Context (archaeology); Tree breeding; Biology; Computer science; Abiotic component; Data science; Ecology; Woody plant","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":[],"consensus_categories":[],"category_scores_codex":[0.001566744,0.0001043499,0.0002008489,0.0001043713,0.0004669895,0.00001787791,0.0006840365,0.00002143507,0.00001940408],"category_scores_gemma":[0.0003508125,0.00008450438,0.00004563242,0.001469368,0.001710999,0.0002144289,0.0005054388,0.0001981633,0.000002495564],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004464767,"about_ca_system_score_gemma":0.00006170249,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001762711,"about_ca_topic_score_gemma":0.00002251262,"domain_scores_codex":[0.9985103,0.00005154318,0.0003461427,0.0003285016,0.0004599486,0.0003035564],"domain_scores_gemma":[0.9990503,0.0001537127,0.0002674439,0.0004597689,0.0000335841,0.00003514975],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.0000504171,0.0001983771,0.1379692,0.001341904,0.00001571628,0.000004720345,0.001406026,0.04909403,0.1003574,0.008227091,0.0007367872,0.7005984],"study_design_scores_gemma":[0.001022179,0.0004351506,0.6944067,0.006300169,0.00008437293,0.00003454074,0.005045885,0.05842568,0.1266606,0.08901225,0.01724699,0.001325487],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9825979,0.005239054,0.002372801,0.006413395,0.00003728183,0.0005263024,0.00001289414,0.00004985264,0.002750454],"genre_scores_gemma":[0.9949189,0.000575478,0.004171044,0.0003029557,0.000005636276,0.00001024654,0.000002232098,0.000007250595,0.0000062013],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6992729,"threshold_uncertainty_score":0.6304249,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03691901505682046,"score_gpt":0.3080179755789666,"score_spread":0.2710989605221462,"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."}}