{"id":"W2126031860","doi":"10.1016/j.ecolmodel.2005.02.023","title":"Sequential sampling designs for catching the tail of dispersal kernels","year":2005,"lang":"en","type":"article","venue":"Ecological Modelling","topic":"Weed Control and Herbicide Applications","field":"Agricultural and Biological Sciences","cited_by":32,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Biological dispersal; Sampling (signal processing); Kernel (algebra); Statistics; Seed dispersal; Mathematics; Sampling design; Population; Ecology; Computer science; Biology; Telecommunications","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":[0.0003386545,0.00009905496,0.0001597035,0.000004943791,0.000363576,0.00003705762,0.0002774115,0.00008877498,0.0001849127],"category_scores_gemma":[0.00003323461,0.00003160984,0.0001462277,0.00009536513,0.0000581442,0.00006768346,0.00004323476,0.0001127274,0.00001255035],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002372992,"about_ca_system_score_gemma":0.00000708886,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002481233,"about_ca_topic_score_gemma":0.0003555905,"domain_scores_codex":[0.9991272,0.00003691963,0.0002563878,0.0002162039,0.0001019438,0.0002613098],"domain_scores_gemma":[0.9989652,0.0007766073,0.00009426856,0.0000524485,0.00005869758,0.00005278922],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00008197844,0.0002478208,0.0004467455,0.000007051892,0.00003897017,3.155817e-7,0.0002880044,0.333616,0.5596256,0.04563977,0.0001253226,0.05988245],"study_design_scores_gemma":[0.0004272519,0.0003652482,0.0106779,0.00001755554,0.00008428904,0.000003835451,0.0004437666,0.9205219,0.003870109,0.03976718,0.0234461,0.000374832],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8378431,0.000131937,0.1569708,0.004213878,0.00003471781,0.0003939606,0.00002139448,0.00004577506,0.0003444311],"genre_scores_gemma":[0.9927106,0.000008847338,0.006221821,0.0003264406,0.0005262482,0.0001074072,0.00001630843,0.000001026331,0.0000812576],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.586906,"threshold_uncertainty_score":0.2796369,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1106078623793511,"score_gpt":0.2816963895611963,"score_spread":0.1710885271818453,"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."}}