{"id":"W6926112155","doi":"10.21979/n9/zq85ki","title":"GaussianAnything: Interactive Point Cloud Latent Diffusion for 3D Generation","year":2025,"lang":"en","type":"dataset","venue":"DR-NTU (Data)","topic":"Microbial Natural Products and Biosynthesis","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Autoencoder; Point (geometry); Space (punctuation); Point cloud; Face (sociological concept); Latent heat","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":[0.001082789,0.003392944,0.001363371,0.002653408,0.0006964142,0.001665303,0.004675221,0.002391978,0.01008643],"category_scores_gemma":[0.002436831,0.0008798817,0.003428439,0.00230899,0.000752522,0.001150635,0.003039161,0.002214587,0.00952726],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001348071,"about_ca_system_score_gemma":0.001095927,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01618885,"about_ca_topic_score_gemma":0.04595762,"domain_scores_codex":[0.9990174,0.0001517567,0.00006541513,0.0002816179,0.0003916013,0.00009234985],"domain_scores_gemma":[0.9992076,0.0002080625,0.00003991612,0.0003741431,0.0001259789,0.00004429522],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0008848707,0.0005783709,0.004380956,0.002348802,0.0005663704,0.0006303805,0.0002185433,0.1237521,0.01091858,0.006383242,0.5857484,0.2635895],"study_design_scores_gemma":[0.0006784501,0.0002291814,0.003625411,0.0002264513,0.0001187632,0.001088234,0.0001588297,0.686963,0.02326599,0.01490911,0.2685395,0.0001970631],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0270162,0.004716478,0.2675875,0.001285922,0.001201736,0.001402606,0.5290418,0.1557014,0.01204629],"genre_scores_gemma":[0.05233013,0.0008701524,0.2336371,0.0003733841,0.00007456559,0.0007561912,0.7047433,0.002127411,0.005087784],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.01618885,"threshold_uncertainty_score":0.03374243,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04961767037592291,"score_gpt":0.3205269884948319,"score_spread":0.270909318118909,"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."}}