{"id":"W4312892643","doi":"10.1130/abs/2022am-383749","title":"RESTORING THE DUNES: USING SATELLITE IMAGERY TO HELP RESTORE TURTLE NESTING GROUNDS IN PUERTO RICO AFTER HURRICANE MARIA","year":2022,"lang":"en","type":"article","venue":"Abstracts with programs - Geological Society of America","topic":"Remote Sensing and Land Use","field":"Earth and Planetary Sciences","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"ConocoPhillips (Canada)","funders":"","keywords":"Nesting (process); Satellite imagery; Turtle (robot); Satellite; Geography; Remote sensing; Environmental science; Ecology; Engineering; Biology","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007113552,0.0002251841,0.0003397089,0.00003971534,0.0004223595,0.00008382153,0.0002970965,0.00007469397,0.0001904006],"category_scores_gemma":[0.00007402524,0.0001357428,0.0001206468,0.000951309,0.0003779709,0.0001055567,0.00008711438,0.0005844268,0.000009807468],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002533811,"about_ca_system_score_gemma":0.00006442157,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02447037,"about_ca_topic_score_gemma":0.0007727522,"domain_scores_codex":[0.997801,0.0001945983,0.0003718191,0.0004475792,0.000547968,0.0006370503],"domain_scores_gemma":[0.9988847,0.0003556755,0.0002418172,0.0003067158,0.00005207031,0.0001589728],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0002819178,0.0001085025,0.6693711,0.00002660528,0.00003085445,0.0001612618,0.001650814,0.03894967,0.00007152579,7.025055e-7,0.0001011223,0.2892459],"study_design_scores_gemma":[0.0002422227,0.0009160677,0.9640536,0.00005471288,0.00002455088,0.00008906875,0.003948934,0.005108661,0.000008498359,0.00007970327,0.02518874,0.0002852052],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9963925,0.0004556021,0.00007150989,0.001636571,0.0001312658,0.0003740721,0.000005654455,0.00006656485,0.0008662927],"genre_scores_gemma":[0.9669584,0.00005156696,0.03157003,0.001162233,0.0001112703,0.000005751592,0.00003401618,0.000008858334,0.00009785837],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2946825,"threshold_uncertainty_score":0.9820257,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02670418059816101,"score_gpt":0.2366876585132094,"score_spread":0.2099834779150484,"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."}}