{"id":"W2965287316","doi":"10.1002/eap.1988","title":"Comparing generalized and customized spread models for nonnative forest pests","year":2019,"lang":"en","type":"article","venue":"Ecological Applications","topic":"Forest Insect Ecology and Management","field":"Environmental Science","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"U.S. Forest Service","keywords":"Predictive power; Context (archaeology); Biological dispersal; Ecology; Generality; Lymantria dispar; Biology; Predictive modelling; Computer science; Machine learning; Population","routes":{"ca_aff":true,"ca_fund":false,"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":[0.0001653085,0.00009684916,0.0001680108,0.00001868909,0.0001742913,0.00001568671,0.000165688,0.0000745442,0.0007883238],"category_scores_gemma":[0.00001304033,0.00007939616,0.00004175697,0.00009479404,0.0001342502,0.00009911774,0.0002112089,0.0000675406,0.0005805339],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007222084,"about_ca_system_score_gemma":0.000003600753,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000273088,"about_ca_topic_score_gemma":0.0004636207,"domain_scores_codex":[0.9992576,0.00002441712,0.0001309077,0.0003158155,0.00006333936,0.0002079675],"domain_scores_gemma":[0.9995338,0.0001673255,0.00006109908,0.0001649814,0.000007873296,0.00006496906],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","study_design_scores_codex":[0.0001878436,0.000737787,0.2401142,0.00003349103,0.00006539713,0.000001237513,0.0002327559,0.1042158,0.001575036,0.641112,0.009141365,0.002583052],"study_design_scores_gemma":[0.00217423,0.0001633778,0.6000072,0.000002321402,0.00003354298,0.000003046251,0.00004014697,0.1941994,0.00004936745,0.1576995,0.04538352,0.0002443655],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9299445,0.00001339599,0.03983491,0.0004468139,0.00004206202,0.002281806,0.000006492438,0.00006646165,0.02736352],"genre_scores_gemma":[0.9852889,0.00001386023,0.01016744,0.0004285899,0.00001726569,0.002291436,0.00002432235,0.00000647568,0.001761755],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4834124,"threshold_uncertainty_score":0.8631593,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02181298555142514,"score_gpt":0.2475249406191857,"score_spread":0.2257119550677606,"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."}}