{"id":"W4387603951","doi":"10.1093/mnras/stad3098","title":"WD 0141−675: a case study on how to follow-up astrometric planet candidates around white dwarfs","year":2023,"lang":"en","type":"article","venue":"Monthly Notices of the Royal Astronomical Society","topic":"Stellar, planetary, and galactic studies","field":"Physics and Astronomy","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal","funders":"Horizon 2020 Framework Programme; Nuclear Safety and Security Commission; Science and Technology Facilities Council; Vedecká Grantová Agentúra MŠVVaŠ SR a SAV; European Commission; Royal Society; H2020 European Research Council; European Space Agency; National Aeronautics and Space Administration; National Science Foundation","keywords":"Physics; Exoplanet; Planet; Radial velocity; Astrophysics; Photometry (optics); Brown dwarf; White dwarf; Astronomy; Gas giant; Planetary system; Planetary mass; Giant planet; Infrared excess; Stars","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":[0.0005541299,0.0002492936,0.0001972055,0.0009066175,0.0008583787,0.0006315832,0.0004048962,0.0004227409,0.001066776],"category_scores_gemma":[0.001379161,0.00009213198,0.0002008827,0.0005615564,0.0002688576,0.0005077612,0.0006480335,0.00022283,0.0004692124],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003175627,"about_ca_system_score_gemma":0.000321163,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005608935,"about_ca_topic_score_gemma":0.009708385,"domain_scores_codex":[0.9997569,0.00004240501,0.00002238862,0.00006860037,0.00005788256,0.00005181483],"domain_scores_gemma":[0.9992472,0.0001466168,0.0001185954,0.0001197997,0.000166439,0.0002013966],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"case_report","study_design_scores_codex":[0.0001677678,0.0001193982,0.861057,0.0001123133,0.00002930018,0.02877038,0.00299085,0.0009906975,0.02025805,0.0007733713,0.00166355,0.08306725],"study_design_scores_gemma":[0.0000299738,0.0008452125,0.8008527,0.0001993081,0.0001636576,0.0589848,0.01796133,0.01195145,0.03446466,0.003034785,0.07143851,0.00007348251],"study_design_candidate":"case_report","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.994548,0.0002764404,0.002372119,0.0002637687,0.00001649871,0.00002830506,0.0001246347,0.00005065131,0.002319667],"genre_scores_gemma":[0.9917353,0.0001863626,0.006351374,0.00006127408,0.00001708618,0.00001035535,0.0003577266,0.00002071874,0.001259832],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.005608935,"threshold_uncertainty_score":0.01115263,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01548616719099718,"score_gpt":0.2279063191180543,"score_spread":0.2124201519270571,"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."}}