{"id":"W2027704785","doi":"10.1139/x00-103","title":"Creating continuous areas of old forest in long-term forest planning","year":2000,"lang":"en","type":"article","venue":"Canadian Journal of Forest Research","topic":"Forest Management and Policy","field":"Environmental Science","cited_by":96,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Habitat; Ecology; Forest management; Old-growth forest; Geography; Forest road; Range (aeronautics); Forest ecology; Enhanced Data Rates for GSM Evolution; Environmental science; Agroforestry; Forestry; Ecosystem; Computer science; Biology","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.001624503,0.0001675623,0.0003198317,0.0007316956,0.0002244811,0.0001143666,0.0008190626,0.000106188,0.006512918],"category_scores_gemma":[0.0002796757,0.0001541235,0.0001124179,0.0008505531,0.0006136599,0.0004076762,0.00006804304,0.0006259409,0.0002871484],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004360659,"about_ca_system_score_gemma":0.0002859585,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.08403961,"about_ca_topic_score_gemma":0.5389462,"domain_scores_codex":[0.9973369,0.0001493852,0.0006078616,0.000214441,0.0006959277,0.0009955147],"domain_scores_gemma":[0.9985361,0.0001891962,0.0001680217,0.0003022625,0.00006285073,0.0007416235],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00006040283,0.0000317582,0.9742388,0.00002931076,0.0000161603,0.000838282,0.0009518991,0.01279156,0.0000119473,0.0004800828,0.00484258,0.005707205],"study_design_scores_gemma":[0.0006578242,0.00031432,0.9921626,0.0003358891,0.000008295154,0.00007352067,0.00008922129,0.0003936162,0.00001853102,0.0008263052,0.004976339,0.0001435063],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9243057,0.0002164709,0.000005858594,0.0002489844,0.00005912467,0.0002424062,0.000006335666,0.000003750154,0.07491133],"genre_scores_gemma":[0.9910513,0.00004048507,0.00009117314,0.00005358971,0.0001575888,0.000006419186,0.000007323525,0.00002639794,0.008565706],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4549066,"threshold_uncertainty_score":0.9943953,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0331890178967023,"score_gpt":0.3132041700731871,"score_spread":0.2800151521764848,"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."}}