{"id":"W1995244049","doi":"10.1016/j.compag.2004.02.004","title":"A stem analysis computational algorithm for estimating volume growth and its empirical evaluation under various sampling strategies","year":2004,"lang":"en","type":"article","venue":"Computers and Electronics in Agriculture","topic":"Forest ecology and management","field":"Environmental Science","cited_by":5,"is_retracted":false,"has_abstract":false,"ca_institutions":"Natural Resources Canada; Canadian Forest Service","funders":"Canadian Forest Service","keywords":"Frustum; Mathematics; Cross section (physics); Computation; Sampling (signal processing); Interpolation (computer graphics); Volume (thermodynamics); Algorithm; Cylinder; Tree (set theory); Geometry; Statistics; Mathematical analysis; Computer science; Artificial intelligence; Physics; Detector","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0002250753,0.0001062649,0.0001363997,0.00004459607,0.0001703491,0.00007015475,0.00006772152,0.00007130887,0.000006883538],"category_scores_gemma":[0.000003702623,0.00008451522,0.00003203957,0.0002892299,0.00003522714,0.0001469878,0.00007397026,0.0001112397,0.000001428328],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001916512,"about_ca_system_score_gemma":0.00002259882,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003174975,"about_ca_topic_score_gemma":0.0003212001,"domain_scores_codex":[0.9992006,0.0000270676,0.000139567,0.0002796829,0.000138059,0.0002149999],"domain_scores_gemma":[0.9998029,0.00004969356,0.00005433245,0.00003702101,0.00001669222,0.0000393812],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000002916507,0.00004153237,0.00107616,0.000008907026,0.00007442717,8.524094e-7,0.000322975,0.9805561,0.00001396255,0.01090662,0.0001288332,0.006866768],"study_design_scores_gemma":[0.0005106041,0.000101169,0.06223296,0.000006461309,0.0001153653,0.000006637269,0.0001089565,0.8754867,0.000003459225,0.06125778,0.00005431774,0.0001156171],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.207852,0.0002861996,0.7910372,0.0004324732,0.00004414627,0.0002789476,0.000002124838,0.00001596186,0.000050923],"genre_scores_gemma":[0.8829328,0.00002301576,0.1167183,0.0001970894,0.00002403793,0.00003298233,0.00005291879,0.000003592831,0.00001528317],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6750808,"threshold_uncertainty_score":0.3446431,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01681577688069713,"score_gpt":0.2729698791623776,"score_spread":0.2561541022816805,"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."}}