{"id":"W6949210076","doi":"10.5281/zenodo.14060060","title":"Experimental data of Aquistore core sample","year":2024,"lang":"en","type":"dataset","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"","field":"","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Sample (material); Experimental data; Core (optical fiber); Calibration; Data acquisition","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":[0.001421199,0.002623435,0.001560425,0.002712924,0.0008185515,0.001256034,0.00219241,0.002279778,0.04969427],"category_scores_gemma":[0.005329828,0.0006415555,0.001588251,0.00360948,0.0007625758,0.001047043,0.001333801,0.001375285,0.0699967],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009941497,"about_ca_system_score_gemma":0.002151828,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01394645,"about_ca_topic_score_gemma":0.02663524,"domain_scores_codex":[0.9983967,0.0003105531,0.0001070509,0.0005204888,0.0004563864,0.0002088252],"domain_scores_gemma":[0.99775,0.0006645239,0.0001066111,0.0007732866,0.0005242126,0.0001814974],"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":[0.0003080929,0.000114176,0.001242158,0.0008043645,0.00007766792,0.00006109138,0.00002712114,0.002443172,0.001037162,0.000581927,0.9863636,0.00693947],"study_design_scores_gemma":[0.0009411366,0.0001603674,0.01253081,0.0003436106,0.0001828221,0.0002702967,0.0001884055,0.007636169,0.005164745,0.005247444,0.9672108,0.0001232336],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.001989099,0.0003189194,0.0006605177,0.000114155,0.00008937959,0.00004285463,0.9928468,0.001811784,0.002126473],"genre_scores_gemma":[0.001698186,0.00006594656,0.0007830332,0.00004901396,0.00001061779,0.00008969664,0.9959753,0.000189763,0.001138508],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.04969427,"threshold_uncertainty_score":0.1662439,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1318640861318528,"score_gpt":0.3308937524893908,"score_spread":0.199029666357538,"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."}}