{"id":"W3016996470","doi":"","title":"On the Calibration of Multi-object Spectrographs","year":2007,"lang":"en","type":"article","venue":"NPARC","topic":"Astronomy and Astrophysical Research","field":"Physics and Astronomy","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Calibration; Object (grammar); Remote sensing; Computer science; Artificial intelligence; Geography; Mathematics; Statistics","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00722316,0.0007702495,0.0006857302,0.002197921,0.0009082015,0.001879037,0.002363182,0.00188803,0.004291473],"category_scores_gemma":[0.02243236,0.0007595678,0.0007307645,0.002639313,0.0008094724,0.002506667,0.001967707,0.002262091,0.003309747],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001400322,"about_ca_system_score_gemma":0.001057671,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005329841,"about_ca_topic_score_gemma":0.008281386,"domain_scores_codex":[0.9957724,0.001431933,0.0001214749,0.0008013989,0.001716994,0.0001558026],"domain_scores_gemma":[0.9870064,0.003404206,0.0006347289,0.005293027,0.00343568,0.0002258982],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0004810795,0.0001096733,0.02378834,0.0003823022,0.0002634602,0.0001399895,0.0003259098,0.08111873,0.02773733,0.07655568,0.03346158,0.755636],"study_design_scores_gemma":[0.0001397334,0.0001696263,0.1402687,0.0007492811,0.0004067334,0.001322458,0.0003419657,0.4644185,0.09475811,0.1290023,0.1681214,0.0003011466],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03310452,0.004415444,0.9128855,0.001595039,0.001582496,0.0001468881,0.001439867,0.004372165,0.0404581],"genre_scores_gemma":[0.4182104,0.002387566,0.5587593,0.0008950076,0.0004896087,0.000113418,0.003594125,0.001803946,0.01374682],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00722316,"threshold_uncertainty_score":0.0382002,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01589206109594936,"score_gpt":0.2797623051401852,"score_spread":0.2638702440442359,"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."}}