{"id":"W4300863727","doi":"10.21203/rs.3.rs-2097460/v1","title":"Comparison and Parallel Implementation of Alternative Moving-Window Metrics of the Connectivity of Protected Areas Across Large Landscapes","year":2022,"lang":"en","type":"preprint","venue":"Research Square","topic":"Wildlife-Road Interactions and Conservation","field":"Environmental Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University; Environment and Climate Change Canada","funders":"Environment and Climate Change Canada","keywords":"Metric (unit); Raster graphics; Context (archaeology); Biological dispersal; Computer science; Spatial contextual awareness; Polygon (computer graphics); Spatial ecology; Landscape connectivity; Statistics; Geography; Cartography; Ecology; Mathematics; Artificial intelligence","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00165365,0.0001163781,0.0002873249,0.0001203846,0.0002399787,0.00002554018,0.0003730249,0.00007476346,0.0008863749],"category_scores_gemma":[0.0004105461,0.00009284476,0.00009304799,0.0006518672,0.0001778817,0.0001113936,0.002216199,0.000617086,0.000001218149],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002133198,"about_ca_system_score_gemma":0.00007527215,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01395667,"about_ca_topic_score_gemma":0.003173352,"domain_scores_codex":[0.9973274,0.0006752108,0.0004044544,0.0003051864,0.001029179,0.0002585886],"domain_scores_gemma":[0.998481,0.0004130082,0.0005152172,0.0003813404,0.0001701675,0.00003927782],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00007597092,0.0002376615,0.9884999,0.0002352318,0.00005417586,4.229896e-7,0.003367126,0.002956258,0.002157449,0.0001281906,0.000200998,0.002086598],"study_design_scores_gemma":[0.0004515737,0.0001907942,0.9712822,0.00009178166,0.00001133674,6.684919e-7,0.009411972,0.00684117,0.01086176,0.0004549428,0.000318701,0.00008309534],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9970058,0.00007643567,0.0004957596,0.0002136337,0.00006163133,0.001288986,0.0006374098,0.000006713362,0.0002136795],"genre_scores_gemma":[0.9994877,0.00005330941,0.0001742583,0.000005655108,0.00001257569,0.0001549636,0.00005708391,0.000009767221,0.00004470093],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01721771,"threshold_uncertainty_score":0.9926095,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06411257247384572,"score_gpt":0.4284971123874939,"score_spread":0.3643845399136482,"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."}}