{"id":"W4391984163","doi":"10.1016/j.foreco.2024.121749","title":"Iterative nearest neighbour age-height curve adjustment: Addressing the impact of spatial heterogeneity on longitudinal forestry provenance trial data","year":2024,"lang":"en","type":"article","venue":"Forest Ecology and Management","topic":"Forest ecology and management","field":"Environmental Science","cited_by":2,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of British Columbia","funders":"Faculty of Forestry, University of British Columbia; Ministry of Forests, Lands, Natural Resource Operations and Rural Development; Natural Sciences and Engineering Research Council of Canada; University of British Columbia","keywords":"Provenance; Longitudinal data; Spatial heterogeneity; Forestry; Geography; Physical geography; Environmental science; Ecology; Econometrics; Statistics; Mathematics; Geology; Computer science; Biology; Data mining","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.1208752,0.0008038216,0.002745637,0.001301922,0.002121106,0.002365115,0.005486513,0.002297747,0.00270312],"category_scores_gemma":[0.2823536,0.0008925093,0.002465008,0.003719862,0.001687293,0.002670064,0.003008518,0.002811356,0.0004884343],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001376002,"about_ca_system_score_gemma":0.004613652,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01982772,"about_ca_topic_score_gemma":0.04111628,"domain_scores_codex":[0.8895081,0.08701854,0.007490573,0.009451902,0.005142651,0.001388209],"domain_scores_gemma":[0.7146675,0.2276599,0.01169752,0.03465312,0.009977219,0.001344766],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.007818406,0.001040036,0.4097091,0.001326514,0.01506753,0.0008260383,0.003773659,0.09446114,0.004683904,0.01325562,0.008945027,0.4390931],"study_design_scores_gemma":[0.001474854,0.001951635,0.1136504,0.000337812,0.004637845,0.001103263,0.001058873,0.8175357,0.006495517,0.03332732,0.01814555,0.00028136],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2122092,0.001497705,0.7802618,0.000495828,0.0003208653,0.0009990464,0.001074619,0.001483437,0.001657499],"genre_scores_gemma":[0.6967337,0.0001617368,0.29761,0.000275781,0.00006803295,0.001175443,0.001512208,0.0004998576,0.001963226],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.1208752,"threshold_uncertainty_score":0.6392568,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04994329265620862,"score_gpt":0.3159099352862275,"score_spread":0.2659666426300189,"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."}}