{"id":"W4398785204","doi":"10.2305/wicl5376","title":"Geovisualisation for effective management of invasive species: Bridging the knowing–doing gap","year":2024,"lang":"en","type":"article","venue":"PARKS","topic":"Species Distribution and Climate Change","field":"Environmental Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Bridging (networking); Invasive species; Business; Environmental resource management; Ecology; Computer science; Biology; Environmental science","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.006319809,0.0005074731,0.000228736,0.001197257,0.00379502,0.005167233,0.0009405889,0.001147328,0.007668806],"category_scores_gemma":[0.01260239,0.0002027717,0.0002944441,0.0007931745,0.005317292,0.003317765,0.005426207,0.001756083,0.0005568991],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.004246708,"about_ca_system_score_gemma":0.005602141,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03790912,"about_ca_topic_score_gemma":0.08928515,"domain_scores_codex":[0.9975572,0.001641466,0.0000581786,0.00008098595,0.0003314024,0.0003307984],"domain_scores_gemma":[0.9899885,0.007683531,0.0004505606,0.0005328385,0.0005500386,0.0007944666],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"qualitative","study_design_gemma":"observational","study_design_scores_codex":[0.0002114155,0.0003601007,0.01239127,0.002094735,0.00003313152,0.001162284,0.6023337,0.001604644,0.008995746,0.03197899,0.02344709,0.3153869],"study_design_scores_gemma":[0.00009650396,0.0003022669,0.0319098,0.003140297,0.0000923063,0.001067721,0.5083298,0.003179067,0.003391951,0.02898142,0.4193578,0.0001510677],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6691718,0.003181011,0.05131537,0.04490799,0.0005772354,0.0007189359,0.0003719803,0.001097172,0.2286585],"genre_scores_gemma":[0.9699336,0.001361008,0.02167583,0.001028901,0.00006383652,0.0002242751,0.00007382623,0.0000655028,0.005573079],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03790912,"threshold_uncertainty_score":0.07537693,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03092502542958565,"score_gpt":0.2859103589567327,"score_spread":0.2549853335271471,"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."}}