{"id":"W4415836408","doi":"10.21203/rs.3.rs-7801575/v1","title":"Marine cold-spells in the Philippines (1982–2021): a systematic characterization of metrics, trends, and asymmetries with marine heatwaves","year":2025,"lang":"","type":"preprint","venue":"Research Square","topic":"Climate variability and models","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Victoria","funders":"","keywords":"Climate change; Vulnerability (computing); Baseline (sea); Global warming; Ecosystem; Sea surface temperature; Vulnerability assessment; Indian ocean","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":[0.0004119985,0.0003300363,0.00018343,0.001584618,0.0002700689,0.0005686526,0.000283996,0.0002390989,0.001259634],"category_scores_gemma":[0.001366912,0.0001343585,0.0003183396,0.003024286,0.0002121433,0.0005737708,0.0006559298,0.0002421238,0.0002547068],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004727352,"about_ca_system_score_gemma":0.00038933,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.05619097,"about_ca_topic_score_gemma":0.06858806,"domain_scores_codex":[0.9998771,0.00001806492,0.00001378465,0.00004546523,0.00002134936,0.00002418133],"domain_scores_gemma":[0.9988658,0.0001332194,0.0004919239,0.000108637,0.0002653935,0.0001350154],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00006951344,0.000009942657,0.9872217,0.00007956876,0.0001662404,0.0001012193,0.0005485819,0.00109219,0.0009983361,0.0001508969,0.001371302,0.008190639],"study_design_scores_gemma":[6.595491e-7,0.000005441472,0.9986382,0.000007240632,0.000013973,0.00002434279,0.0001356915,0.0003644913,0.00008133773,0.00001537256,0.0007105449,0.000002590134],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9898899,0.0006609468,0.0002200216,0.00009335852,0.000009779666,0.000005652399,0.008133581,0.00004045901,0.0009463031],"genre_scores_gemma":[0.9941249,0.0002859441,0.0001441831,0.00001372042,0.00001477601,0.000008700842,0.005135919,0.00001043406,0.0002614473],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.05619097,"threshold_uncertainty_score":0.1117278,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04241898536672781,"score_gpt":0.3253376521047584,"score_spread":0.2829186667380306,"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."}}