Interview with Lieutenant Commander Timothy Flath
Notice bibliographique
Résumé
Narrator: Lieutenant Commander Timothy Flath Interviewer: John Thomson Interview Date and Location Thursday, February 29, 2024 - Royal Canadian Legion, Langford, British Columbia, In-person Synopsis of the Interview: Lieutenant Commander Flath was born in 1960 in North Battleford, Saskatchewan. His father was in the Canadian military so his family lived in many different locations. He joined the RCN in Calgary, Alberta in 1986 and he completed his initial training at CFOCS Chilliwack, B.C. Flath held many different positions in the navy, including Maritime Surface Officer (MARS), Bridge Watchkeeper, Above Water Weapons Officer, and Dive Officer, before qualifying as a Clearance Diver. He explains that he became a Clearance Diver because it was an interesting and challenging profession. Flath was never bored, there were always projects to complete, there was never any “down time”, and he was tasked with many responsibilities. He was also able to participate in exchanges with the U.S. Navy in order further his training and to learn about their procedures and practises. Flath discusses his participation in the recovery of military munitions from a lake in the Yukon and he mentions the role of Clearance Divers in Afghanistan, in the recovery of items from the Swiss Air 111 disaster, and the assistance to a tragic collision between a fishing vessel and a barge in Active Pass, B.C. In addition, he discusses his role as a ship’s diver in the recovery of a large number of people on a derelict vessel near Vietnam. Flath concludes the interview by highlighting his participation in the scuttling of a U.S. aircraft carrier near Hawaii and by highly recommending, to young people, a career as a Clearance Diver in the RCN. Interview Time Log: 00:59 - 01:49 – Early life before joining the military. 01:50 – 02:50 – Inspiration for joining the military. 03:11- 03:32 – Initial training in Chilliwack, British Columbia (CFOCS). 03:33 – 04:30 – Postings after initial training: Gunnery Officer, driving a warship, Naval Boarding Officer, Ship’s Team Diver. Motivation to become a Clearance Diver. 05:31 – 06:56 – Explanation of duties of a Ship’s Team Diver. 06:57 – 08:00 – Reason for getting into Clearance Diving. 08:06 – 10:52 – Responsibilities as a Clearance Diver. 10:55 – 16:14 – Role as a Demolitions Officer relating to Clearance Diving. 16:30 – 19:34 – EOD Officer exchange with the U.S. Navy. 19:36 – 21:33 – Assisting other Branches of the CAF. 21:46 – 26:51 – Routine, everyday tasks of a Clearance Diver. 26:52 – 35:10 – Most difficult task, emotionally and/or physically, that a Clearance Diver would be required to undertake. 35:12 – 38:39 – Involvement in “historically significant incidents”. 38:41 – 43:48 – Participation in recovery of Boat People near Vietnam. 44:13 – 50:50 – Career Highlights. 50:55 – 54:22 – Concluding comments. Significant Stand-Outs in the Interview: Routine, everyday tasks of a Clearance Diver. EOD, Lake La Barge, Yukon. Swiss Air 111 disaster. Fishing boat/barge collision in Active Pass, B.C. Scuttling a U.S. aircraft carrier assault ship.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,002 | 0,009 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,002 | 0,002 |
| Études des sciences et des technologies | 0,015 | 0,003 |
| Communication savante | 0,003 | 0,003 |
| Science ouverte | 0,001 | 0,003 |
| Intégrité de la recherche | 0,004 | 0,006 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,051 | 0,013 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».