{"id":"W7139781559","doi":"","title":"ECODATA : A toolbox to efficiently explore and communicate animal movements alongside environmental and anthropogenic context using geospatial big data","year":2025,"lang":"en","type":"article","venue":"KOPS (University of Konstanz)","topic":"Wildlife-Road Interactions and Conservation","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Geospatial analysis; Toolbox; Bespoke; Context (archaeology); Software; Process (computing); Wildlife; Relation (database); Big data","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001223186,0.0001117372,0.000150493,0.00007618326,0.0003932172,0.00002721077,0.0004436429,0.00004310079,0.0004160743],"category_scores_gemma":[0.00001244843,0.0001380476,0.00002247036,0.0001507954,0.0003965837,0.0004765403,0.001725602,0.00008955014,0.00002262242],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001214594,"about_ca_system_score_gemma":0.00001824927,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005010482,"about_ca_topic_score_gemma":0.003688194,"domain_scores_codex":[0.9991667,0.00004288196,0.0001283034,0.0003531676,0.000148732,0.0001601819],"domain_scores_gemma":[0.999211,0.00004149264,0.00008218224,0.0005764532,0.000005282886,0.00008360078],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0009299968,0.0007969333,0.5586485,0.00005009991,0.0002855747,0.00006569576,0.006492991,0.0003040366,0.1608111,0.0002768417,0.007120364,0.2642178],"study_design_scores_gemma":[0.001930326,0.0002463668,0.9016742,0.0001344513,0.000158101,0.00002630126,0.034123,0.0360101,0.0007448854,0.00005506992,0.02447174,0.0004255188],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9961733,0.00007905581,0.002116032,0.0005699504,0.00006680853,0.0002158328,0.0002495427,0.00001256538,0.0005169539],"genre_scores_gemma":[0.9975748,0.00007890046,0.001784688,0.000251345,0.000005825639,2.768101e-7,0.00007325817,0.000005253888,0.0002256197],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3430256,"threshold_uncertainty_score":0.7574384,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05726844357487048,"score_gpt":0.2473638942227473,"score_spread":0.1900954506478768,"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."}}