{"id":"W6948582912","doi":"10.5061/dryad.ttdz08m1f","title":"Global 100m Terrestrial Human Footprint (HFP-100)","year":2023,"lang":"en","type":"dataset","venue":"Open MIND","topic":"Amphibian and Reptile Biology","field":"Environmental Science","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"Impact","funders":"","keywords":"Footprint; Ecological footprint; Biodiversity; Satellite imagery; Software; Land use; Satellite","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009448442,0.001435262,0.0007819035,0.001969955,0.0005098177,0.001303952,0.002283839,0.001366145,0.02341226],"category_scores_gemma":[0.003328032,0.0004412348,0.0008090147,0.00466551,0.0003553156,0.001062119,0.0014433,0.001252935,0.02974455],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009714211,"about_ca_system_score_gemma":0.00137163,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.04058731,"about_ca_topic_score_gemma":0.0706488,"domain_scores_codex":[0.9994525,0.0001019272,0.0000692278,0.0001528084,0.0001456289,0.00007792506],"domain_scores_gemma":[0.999003,0.0002302294,0.0001290647,0.0002180072,0.0003130015,0.0001067151],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00004206743,0.0000241637,0.002651237,0.0005243896,0.00005473979,0.00005326465,0.00004866642,0.0009204588,0.0001451134,0.0008963364,0.9903499,0.004289754],"study_design_scores_gemma":[0.0002112228,0.00001734386,0.01524581,0.0003261906,0.00003142185,0.0001320477,0.0001783081,0.001937847,0.0004726818,0.002511791,0.9788855,0.00004980506],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00027818,0.00004155558,0.0001739533,0.00005701563,0.00001364932,0.000009661026,0.9986213,0.000227093,0.0005775028],"genre_scores_gemma":[0.0006854479,0.00003223149,0.000527338,0.00003036472,0.000003123276,0.00005004691,0.998384,0.00003305755,0.0002544196],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.04058731,"threshold_uncertainty_score":0.08070213,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05207672960894565,"score_gpt":0.3363343127848203,"score_spread":0.2842575831758746,"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."}}