{"id":"W4200220473","doi":"10.12688/openreseurope.13671.2","title":"The Salem simulator version 2.0: a tool for predicting the productivity of pure and mixed forest stands and simulating management operations","year":2021,"lang":"en","type":"article","venue":"Open Research Europe","topic":"Forest ecology and management","field":"Environmental Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Natural Resources Canada; Canadian Forest Service","funders":"Horizon 2020 Framework Programme; National Institutes of Health; National Institute of Environmental Health Sciences; Agence de la transition écologique; Office National des Forêts; European Commission; Narodowe Centrum Nauki; Fachagentur Nachwachsende Rohstoffe; Ministrstvo za Izobraževanje, Znanost in Šport; Agence Nationale de la Recherche","keywords":"Productivity; Forest management; Perspective (graphical); Forest inventory; Tree (set theory); Computer science; Environmental resource management; Ecology; Environmental science; Forestry; Geography; Mathematics; Economics; Artificial intelligence; Biology","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":["sts"],"consensus_categories":[],"category_scores_codex":[0.002887738,0.00006437236,0.00007823695,0.00001472232,0.001411619,0.0002226789,0.0003186585,0.0000187926,0.00005573638],"category_scores_gemma":[0.0007311801,0.00003922844,0.00001360736,0.0002467552,0.0003278543,0.0002654048,0.002547507,0.0001308735,0.000007412741],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003594134,"about_ca_system_score_gemma":0.00001719748,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000511662,"about_ca_topic_score_gemma":0.001194544,"domain_scores_codex":[0.9987486,0.0003068986,0.0001387492,0.0002976483,0.0002661144,0.000241967],"domain_scores_gemma":[0.9990306,0.0005260034,0.0000291462,0.00031766,0.00005888864,0.00003762667],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.001174205,0.0007355466,0.4298663,0.0005686546,0.0004585297,0.0001069027,0.00450948,0.2077422,0.005080135,0.2204494,0.03174675,0.0975619],"study_design_scores_gemma":[0.00212979,0.0006227845,0.6537074,0.00008686299,0.00006461075,0.000006368885,0.004432674,0.1415577,0.001428804,0.004666968,0.1910565,0.0002395423],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9871575,0.00007230404,0.0004551354,0.002621594,0.00005215099,0.00186122,0.00001136505,0.000006185246,0.007762515],"genre_scores_gemma":[0.9942361,0.0001202876,0.0006254821,0.00003410111,0.00001764909,0.00009203183,0.000005865779,0.000008901033,0.004859627],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2238411,"threshold_uncertainty_score":0.9998884,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03812121412593273,"score_gpt":0.3258790801461397,"score_spread":0.287757866020207,"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."}}