{"id":"W2778671775","doi":"10.1017/inp.2017.38","title":"Constructing Standard Invasion Curves from Herbarium Data—Toward Increased Predictability of Plant Invasions","year":2017,"lang":"en","type":"article","venue":"Invasive Plant Science and Management","topic":"Plant and animal studies","field":"Agricultural and Biological Sciences","cited_by":23,"is_retracted":false,"has_abstract":true,"ca_institutions":"Algoma University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Herbarium; Invasive species; Introduced species; Standardization; Resource (disambiguation); Predictability; Ecology; Prioritization; Biology; Environmental resource management; Computer science; Engineering; Environmental science; Statistics","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":[0.03840785,0.001547922,0.001245911,0.009303457,0.0008496746,0.003683768,0.001529423,0.001004964,0.00111464],"category_scores_gemma":[0.1153766,0.0007224201,0.001395576,0.006769537,0.0009742345,0.004906598,0.002762421,0.00197388,0.0007543464],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001557033,"about_ca_system_score_gemma":0.001862686,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01074281,"about_ca_topic_score_gemma":0.009021281,"domain_scores_codex":[0.9859527,0.008462871,0.00152006,0.00186915,0.001828263,0.0003668792],"domain_scores_gemma":[0.8145964,0.1166243,0.02607012,0.02538675,0.01544146,0.001880985],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0006105576,0.0002509349,0.5936192,0.0005596181,0.0008024313,0.0001583054,0.001435527,0.141241,0.003299857,0.007047701,0.005836066,0.2451387],"study_design_scores_gemma":[0.00005670523,0.0005261702,0.3047979,0.000228908,0.0001402176,0.0003869199,0.0008670342,0.6502659,0.004344566,0.02119928,0.01688907,0.0002973558],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3837024,0.001045888,0.5895448,0.0006635336,0.0001340443,0.0007798026,0.01155728,0.006620331,0.0059519],"genre_scores_gemma":[0.7229588,0.0005719003,0.2628775,0.0001036399,0.00007664185,0.0005422395,0.01164199,0.0007401896,0.0004871297],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.03840785,"threshold_uncertainty_score":0.2031226,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1236758039973313,"score_gpt":0.2508077233798638,"score_spread":0.1271319193825326,"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."}}