{"id":"W2887265567","doi":"10.1139/cjfr-2018-0053","title":"Aggregating microsegments into harvest blocks by using spatial optimization and proximity objectives","year":2018,"lang":"en","type":"article","venue":"Canadian Journal of Forest Research","topic":"Forest Management and Policy","field":"Environmental Science","cited_by":24,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Adjacency list; Block (permutation group theory); Mathematics; Aggregate (composite); Combinatorics","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":[0.0009065963,0.00009176452,0.000105115,0.0002419347,0.0006242239,0.0002087052,0.0002678248,0.00005600063,0.0009920684],"category_scores_gemma":[0.0002470697,0.00008462913,0.0000261814,0.0003460262,0.0009530958,0.0005011259,0.00009616355,0.0002476559,0.00003558463],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000405787,"about_ca_system_score_gemma":0.0002084173,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.1377676,"about_ca_topic_score_gemma":0.2272688,"domain_scores_codex":[0.9987226,0.0001137417,0.0002116781,0.0001573947,0.0003475024,0.0004470656],"domain_scores_gemma":[0.999206,0.00003613155,0.0001150105,0.0001166487,0.00008729236,0.0004389354],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00002863293,0.00002310233,0.9639141,0.00001557097,0.00002466993,0.00003577665,0.001954434,0.003230507,0.00196775,0.00002996424,0.0181798,0.01059568],"study_design_scores_gemma":[0.005213646,0.004544739,0.6484784,0.001003769,0.0001186036,0.0004447082,0.001340893,0.1958394,0.01355954,0.004679696,0.1230722,0.001704375],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9952248,0.000104113,0.0009075467,0.0002681423,0.0000911136,0.0001753818,0.000004734339,0.000002459494,0.003221699],"genre_scores_gemma":[0.9955929,0.00001535888,0.00313664,0.00005531259,0.0002005779,0.000001492192,0.000002632374,0.00001304584,0.0009820564],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3154357,"threshold_uncertainty_score":0.9999211,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02562859711167937,"score_gpt":0.3034476200088658,"score_spread":0.2778190228971864,"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."}}