{"id":"W4414028853","doi":"10.1101/2025.08.31.673375","title":"The Global Canopy Atlas: analysis-ready maps of 3D structure for the world’s woody ecosystems","year":2025,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Remote Sensing and LiDAR Applications","field":"Environmental Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Natural Resources Canada; Université du Québec en Abitibi-Témiscamingue; Canadian Forest Service; University of British Columbia","funders":"","keywords":"Atlas (anatomy); Canopy; Ecosystem; Geography; Environmental resource management; Environmental science; Ecology; Geology; Archaeology; Biology; Paleontology","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":[0.001735159,0.0007305364,0.0003871349,0.005206174,0.000385218,0.001197372,0.0007946278,0.0003931329,0.005367462],"category_scores_gemma":[0.002433358,0.0003139827,0.0005043503,0.004577263,0.0002405055,0.001307958,0.001283976,0.000613943,0.002470904],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005134076,"about_ca_system_score_gemma":0.0008677358,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01113964,"about_ca_topic_score_gemma":0.01770981,"domain_scores_codex":[0.999297,0.0001774404,0.0000751897,0.0001527044,0.0002390062,0.0000587071],"domain_scores_gemma":[0.9977481,0.0004679756,0.000407277,0.0005638978,0.0006266938,0.0001860579],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.0006224868,0.0005032717,0.1637408,0.001275734,0.0006624293,0.0005444644,0.001100656,0.06715521,0.06001417,0.01438445,0.2926153,0.3973811],"study_design_scores_gemma":[0.0001550533,0.0001699227,0.5454851,0.0003093335,0.0001627085,0.0004842975,0.0008812023,0.1815395,0.02788487,0.01056674,0.2320705,0.0002907971],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"dataset","genre_gemma":"empirical","genre_scores_codex":[0.1672161,0.001052415,0.2609955,0.0005605162,0.0002156767,0.0005058774,0.4857691,0.06485973,0.01882507],"genre_scores_gemma":[0.3093143,0.0003737843,0.4086849,0.0001308152,0.0001192819,0.0005801998,0.2745623,0.003957549,0.00227688],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01113964,"threshold_uncertainty_score":0.02214956,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008250868522373837,"score_gpt":0.2295168213353461,"score_spread":0.2212659528129723,"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."}}