{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001024888,0.0001639551,0.0002950463,0.00002538812,0.001122587,0.0001871546,0.001290088,0.00003763876,0.00003884361],"category_scores_gemma":[0.0006996266,0.00007058818,0.00003024106,0.0001401078,0.00102159,0.0006045811,0.002074679,0.00008816906,0.000003922575],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003346442,"about_ca_system_score_gemma":0.00004299288,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003369794,"about_ca_topic_score_gemma":0.01487678,"domain_scores_codex":[0.9980946,0.00003889139,0.0002778202,0.0006193415,0.0006525472,0.0003168369],"domain_scores_gemma":[0.9989135,0.0003430556,0.0002793887,0.0002031626,0.000118682,0.0001421577],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0002895762,0.0001604497,0.827626,0.0004587782,0.0001338815,0.000122052,0.0002830372,3.998585e-7,0.1254472,0.002564338,0.01235318,0.0305611],"study_design_scores_gemma":[0.0003685642,0.0002124178,0.9776393,0.001133864,0.00008946246,0.00001402768,0.003063257,0.0001183115,0.01079568,0.0007283042,0.00552933,0.0003074572],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9852473,0.0003751402,0.000005251076,0.001484029,0.0001229685,0.0004214331,0.009893265,0.00003375271,0.002416827],"genre_scores_gemma":[0.9944188,0.004661197,0.0002221346,0.0002160656,0.00006497926,0.00001079459,0.0003947683,6.848757e-7,0.00001059119],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1500134,"threshold_uncertainty_score":0.8634143,"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."}}