{"_canonicalization":{"envelope_id":"axm_ + sha256(envelope minus {signature, axiom_id, anchors})","envelope_signature":"ed25519(envelope minus {signature, axiom_id})","json":"sort_keys=True, separators=(',',':'), ensure_ascii=False, allow_nan=False, utf-8","leaf_hash":"sha256(0x00 || canonical_json(envelope_full))","seal_signature":"ed25519(seal minus {signature, sig_algorithm})"},"axiom_id":"axm_43f69a7f27159ad82163f46f32cd7857caea6aad702137cea8302decfbd8b069","bitcoin_anchor":{"bitcoin_attestations":[],"calendar_attestations":[],"ots_url":"","stamped_at":"","status":"pending_next_stamp"},"envelope":{"anchors":[{"chain":"crovia.axiom_graph","height":0,"merkle_proof":"spider_vendor_press_v1","root_at_anchor":"spider_vendor_press_v1"}],"axiom_id":"axm_43f69a7f27159ad82163f46f32cd7857caea6aad702137cea8302decfbd8b069","axiom_type":"AX.OBS","body":{"axiom_subtype":"news.vendor_press.v1","category":"news","fingerprint":"271a956c56916804f557e74b809f11e637f9f9fd95ee5e127415a8ba1d29078d","published":"Fri, 10 Jul 2026 00:00:00 -0400","receipt_hash":"271a956c56916804f557e74b809f11e637f9f9fd95ee5e127415a8ba1d29078d","schema":"spider.news.vendor_press.v1","spider":"vendor_press","spider_record":{"axiom_subtype":"news.vendor_press.v1","category":"news","decision_hint":"POSITIVE","envelope_target":"AX.OBS","fingerprint":"271a956c56916804f557e74b809f11e637f9f9fd95ee5e127415a8ba1d29078d","observed_at":"2026-07-10T04:43:53.465232Z","parent_run_hash":"06997be187ba20932a2030c56de194579eacf484085252a25bee544eab183e91","published":"Fri, 10 Jul 2026 00:00:00 -0400","runtime_version":"0.1.0","schema":"spider.news.vendor_press.v1","source_status":200,"source_url":"https://export.arxiv.org/rss/cs.AI","spider":"vendor_press","summary_excerpt":"arXiv:2607.08429v1 Announce Type: cross \nAbstract: Male infertility is a significant yet often underdiagnosed aspect of reproductive health, with semen analysis serving as the cornerstone of clinical evaluation. To address this problem, this study investigates the use of machine learning algorithms to classify male fertility status based on key semen parameters, i.e., sperm concentration, motility, and morphology, using the VISEM dataset. This dataset includes semen samples from 85 participants, classified into three categories, i.e., Fertile, Sub-Fertile, and Infertile, according to the World Health Organization's criteria. After pre-processing and feature engineering, the dataset was used to train and assess multiple classification models using the LazyPredict framework. Among the more than 40 algorithms tested, the Nearest Centroid classifier achieved an accuracy of 94.2%, outperforming other models such as Support Vector Machines and Quadratic Discriminant Analysis. The model's rob","title":"Predicting Male Fertility Using Machine Learning: A Semen Parameters Based Analysis with the VISEM Dataset","url":"https://arxiv.org/abs/2607.08429","vendor":"arxiv_cs_ai"},"summary":"arXiv:2607.08429v1 Announce Type: cross \nAbstract: Male infertility is a significant yet often underdiagnosed aspect of reproductive health, with semen analysis serving as the cornerstone of clinical evaluation. To address this problem, this study investigates the use of machine learning algorithms to classify male fertility status based on key semen parameters, i.e., sperm concentration, motility, and morphology, using the VISEM dataset. This dataset includes semen samples from 85 participants, classified into three categories, i.e., Fertile, Sub-Fertile, and Infertile, according to the World Health Organization's criteria. After pre-processing and feature engineering, the dataset was used to train and assess multiple classification models using the LazyPredict framework. Among the more than 40 algorithms tested, the Nearest Centroid classifier achieved an accuracy of 94.2%, outperforming other models such as Support Vector Machines and Quadratic Discriminant Analysis. The model's rob","title":"Predicting Male Fertility Using Machine Learning: A Semen Parameters Based Analysis with the VISEM Dataset","vendor":"arxiv_cs_ai"},"confidence":{"method":"deterministic"},"decision":"POSITIVE","issued_at":"2026-07-10T04:43:53Z","notes":"Spider vendor_press (news) news.vendor_press.v1","object":{"captured_by":"crovia.spider.vendor_press","primary_source_url":"https://arxiv.org/abs/2607.08429"},"predecessors":[],"schema":"crovia.axiom.v1","signature":"ed25519:df9640831b3cae07fa20bf68e635f89f998179d18fbbc9ff9dbd905faf63089f523bb646b70e8eb17ca6f9780e53c3f6392d519a5eb01f9a0e68708d2c52d304","signer":"crovia.substrate","subject":{"observed_at":"2026-07-10T04:43:53Z","source_collector":"spider:vendor_press","target_id":"https://arxiv.org/abs/2607.08429"},"tsa":{"authority":"crovia.substrate.bootstrap","rfc3161_token":"{\"kind\":\"crovia.bootstrap.tsa\",\"source_jsonl\":\"/opt/crovia/spider/data/news/vendor_press_v1.jsonl\",\"source_seal_merkle_root\":\"spider_vendor_press_v1\",\"upgrade_path\":\"Sessione H \\u2014 OpenTimestamps weekly anchor\"}"},"zk_mode":"clear","zk_proof":null},"ledger":{"leaf_hash":"cf442f03bdaa0d79856cdd9a64372a5ba6d14bbbb4db5c93d56c32ad197c8226","leaf_index":299486,"ledger_path":"/opt/crovia/substrate/axiom_ledger.jsonl"},"merkle_proof":{"hash_alg":"sha256","leaf_prefix":"0x00","node_prefix":"0x01","odd_leaf_rule":"duplicate_last","path":[{"sibling":"cb4b784853d41b0884fde281850120ac9572ce2f10fe6fe2f2d303c219bc9bc9","side":"right"},{"sibling":"97f998342ed2eccf0e12eabcfde7f8b87a379d9c7f20ed1a1c8174208ddc807b","side":"left"},{"sibling":"00c51383fb131dd6a8c0287e219fc6f5a5c233886b0e19262d70cf22b396837a","side":"left"},{"sibling":"cf96d06c852a3a14e43d9912cdbabc7deb16cd63a7bcd0e833fded9bfda66912","side":"left"},{"sibling":"ab62efea4be81c3e11decc2cfbdf064bba80900866bbc4b2ba6440e38c03c65b","side":"left"},{"sibling":"5d61bb664d795d0b947c9718e3b4cd063fec90ec065b45e78fb40c2b0da9829f","side":"right"},{"sibling":"7a96803ed93140d8d4c0e1fe53c891fc73447ccfce6e55b7c43e7a06979f307a","side":"left"},{"sibling":"b1b72243c904808d4df9b283c65832a729a411520c97fd9d17f891f214971a0a","side":"left"},{"sibling":"2c5ce75ad134aa442d7fbb5dc9968b0f2e48097c2ffaa2111d405de1c4a9c48b","side":"left"},{"sibling":"36a2c507282befad57202dd10278a66d37b402692f195a22d2275b3d1b2488d8","side":"right"},{"sibling":"f80e8d47e0860527b906fc2dba9a52609f7f623f2772479ae922cc019bab36d9","side":"right"},{"sibling":"cae83500ab2c25555aa6b5eaf9232696d15a868d91b34f7531dd955daadf70f7","side":"right"},{"sibling":"64dab64d51bdcb909e2a5e37efb8909d6704ecf824be484b5d2b60ee6e518890","side":"left"},{"sibling":"576f134a23c19a758ae5efd53016092a74b9900e878cf6eb4f3dab6be682b395","side":"right"},{"sibling":"3c65f53d7c3e4feba7c745e8df1327760ffa768eec84336db14d515a31731532","side":"right"},{"sibling":"d8b9143917b539c543cf4448cec00131f8b807bd8004979c54ebe09798748c66","side":"left"},{"sibling":"8728642cdb98496d916cc653f0d919d7eb2e89d0c529927c9e89091074ad584c","side":"right"},{"sibling":"8025674cb002a22ae243ca0c295c18c1d0ee119189ea88e08ac14a3a1468b8e3","side":"right"},{"sibling":"1cecb7f447febd025aac272837c80de218aecc6485d2395a509b2a1f1b9c746e","side":"left"}]},"schema":"crovia.axiom_proof.v1","seal":{"first_collector_run_id":"","first_receipt_hash":"","jsonl_path":"/opt/crovia/substrate/axiom_ledger.jsonl","key_id":"430895f101d38164","last_collector_run_id":"","last_receipt_hash":"","leaf_count":299721,"merkle_root":"f7115d63193d3285ca28cb9f741ecea2513f9b3e492e785f97076f3cf8f9bb98","public_key_hex":"cf742e26f75669dc673cb5c0786a1ae23ae8ca19c347317192ce40c28a7ff25c","run_id":"hourly_json_retrofit_20260710T053701Z","schema":"crovia.seal.v1","seal_family_version":"crovia-seal-family/1","seal_kind":"substrate_batch","sealed_at":"2026-07-10T05:38:19Z","sig_algorithm":"ed25519","signature":"4eeedeb744885bff6523d66b1cb86bde62b36917696367addfd980d90b31011daece2e01b20608e4c0870ec31dd0bcb57d297447f01f153bf0716ad527142b00","signer_version":"1.1.0"},"trust_root":{"key_id":"430895f101d38164","public_key_hex":"cf742e26f75669dc673cb5c0786a1ae23ae8ca19c347317192ce40c28a7ff25c","signature_algorithm":"ed25519","url":"/registry/canon/TRUST_ROOT.md"},"verifier":{"spec":"/registry/canon/AXIOM_RECEIPT_v1.md","url":"/v/axm_43f69a7f27159ad82163f46f32cd7857caea6aad702137cea8302decfbd8b069"}}