{"id":"W2271384268","doi":"10.5849/forsci.15-062","title":"Comparing Tree Selection as Performed by Different Professional Figures","year":2016,"lang":"en","type":"article","venue":"Forest Science","topic":"Forest ecology and management","field":"Environmental Science","cited_by":17,"is_retracted":false,"has_abstract":false,"ca_institutions":"Université de Moncton","funders":"Interreg; European Commission","keywords":"Acre; Tree (set theory); Selection (genetic algorithm); Forestry; Forest management; Agroforestry; Geography; Biology; Mathematics; Computer science; Machine learning","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.007625604,0.0004059412,0.0005359782,0.003157218,0.0007032684,0.001947642,0.0005536861,0.0008268448,0.01414826],"category_scores_gemma":[0.05005289,0.0001637483,0.0007063147,0.002244079,0.0005564786,0.001378019,0.0009009938,0.0006220977,0.002415823],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005884587,"about_ca_system_score_gemma":0.0005621944,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002656496,"about_ca_topic_score_gemma":0.004295654,"domain_scores_codex":[0.9946346,0.00245064,0.0005245132,0.0006004271,0.001312415,0.00047728],"domain_scores_gemma":[0.8981867,0.07836735,0.002722638,0.006578754,0.01204098,0.002103577],"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":[0.01741036,0.001018323,0.3043579,0.0009315417,0.0007393549,0.0008280153,0.006636411,0.02284747,0.04773296,0.01213767,0.03349225,0.5518679],"study_design_scores_gemma":[0.000473777,0.002106081,0.8349664,0.0001790818,0.0004756517,0.001293273,0.008940879,0.07550076,0.03702158,0.009618179,0.02921234,0.0002119425],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9570955,0.0003388526,0.01604732,0.0004478935,0.0002929161,0.00008747508,0.001628223,0.001119256,0.02294263],"genre_scores_gemma":[0.9717436,0.0001566741,0.0170809,0.00008289051,0.0000681348,0.00006343924,0.00222324,0.000618477,0.007962549],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01414826,"threshold_uncertainty_score":0.04733068,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008390239775196082,"score_gpt":0.2325668134339217,"score_spread":0.2241765736587256,"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."}}