{"id":"W2066867864","doi":"10.1139/x07-222","title":"Reconstructing spatial tree point patterns from nearest neighbour summary statistics measured in small subwindows","year":2008,"lang":"en","type":"article","venue":"Canadian Journal of Forest Research","topic":"Forest ecology and management","field":"Environmental Science","cited_by":67,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Statistics; Woodland; Tree (set theory); Nonparametric statistics; Sampling (signal processing); Range (aeronautics); Computer science; Mathematics; Data mining; Ecology","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009550214,0.0002918627,0.0004164995,0.001287255,0.000166692,0.0004116724,0.0004198284,0.0002555385,0.0004262564],"category_scores_gemma":[0.00586575,0.0002658716,0.0005195096,0.0009235532,0.0003110615,0.0007372726,0.0004245473,0.00027936,0.0001678789],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002310374,"about_ca_system_score_gemma":0.000300218,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002563781,"about_ca_topic_score_gemma":0.005680227,"domain_scores_codex":[0.9996179,0.0001369559,0.00003438238,0.00009257701,0.00009635295,0.00002168379],"domain_scores_gemma":[0.9971346,0.00150538,0.0004664881,0.0005176705,0.0003194681,0.00005641535],"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":[0.0005205324,0.000111111,0.1255325,0.0003057282,0.0002995426,0.0002328862,0.0008420846,0.4306643,0.05179933,0.008479136,0.0005649247,0.3806478],"study_design_scores_gemma":[0.00002156733,0.0001394368,0.07416571,0.00002042768,0.00005680161,0.000233159,0.000234478,0.9001977,0.0147565,0.009029976,0.001079624,0.0000646533],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4641276,0.00008559204,0.5347334,0.00002083025,0.000008828876,0.0000243662,0.0002636989,0.0002445111,0.0004911645],"genre_scores_gemma":[0.8331344,0.00006580441,0.1657393,0.000006816891,0.000006659396,0.00002936656,0.0007868366,0.00004732532,0.0001835861],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002563781,"threshold_uncertainty_score":0.005097687,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05030747249754391,"score_gpt":0.2577585910462642,"score_spread":0.2074511185487203,"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."}}