Science response: 2022 oil vulnerability update
Bibliographic record
Abstract
Under Canada’s World Class Tanker Safety System Initiative (WCTSS) a national framework was developed to identify marine biological organisms most vulnerable to ship-source oil (Thornborough et al. 2017) in the event of an oil spill. The Pacific Regional application of this framework (Hannah et al. 2017) identified 27 highly vulnerable biological groups, with sea grasses, salt marsh grasses/succulents, Sea Otters, and baleen whales being most vulnerable. At present, the vulnerability framework is the best tool available for Government of Canada Environmental Incident Coordinators (EICs) to prioritize which species or species assemblages are most vulnerable to oil. EICs use the framework as the foundation to prioritize ‘resources at risk’ for ecological concerns and, consequently, to inform spill response planning processes, emergency response operations during spills, and subsequently to inform mitigation options for impacted species. Additional information that impacts the scoring of species’ vulnerability to oil has become available since the publication of the original application of the Framework in 2017. Science staff, EICs, and other responders require the most up to date assessment of species' vulnerability to the worst-case scenario of whole oil when responding to oil spills. Therefore, to ensure that the assessment includes the most current information, Fisheries and Oceans Canada (DFO) Fish and Fish Habitat Protection Program (FFHPP), Ecosystem Management Branch, requested that Science Branch provide the first update to the Pacific application. Like the original application, this first update to the Pacific application of the oil vulnerability framework will focus on the acute effects of direct contact with crude oil that contains the whole spectrum of oil from very heavy to very light fractions. The assessment, and advice arising from this Canadian Science Advisory Secretariat (CSAS) Science Response (SR), will be used to inform spill response planning processes, emergency response operations during spills in the Pacific Region, as well as mitigation options and other marine spatial planning initiatives. Here, we use “National Framework” when referring to “A framework to assess vulnerability of biological components to ship-source oil spills in the marine environment” (Thornborough et al. 2017), “original Pacific Application” when referring to “Application of a framework to assess vulnerability of biological components to ship-source oil spills in the marine environment in the Pacific Region” (Hannah et al. 2017), and “2022 Pacific Application update” or “the update” when referring to the current document. This Science Response Report results from the regional peer review of April 14, 2022 on the 2022 Update to the application of a framework to assess the vulnerability of biological components to ship-source oil spills in the marine environment in the Pacific Region.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.032 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.009 | 0.008 |
| Insufficient payload (model declined to judge) | 0.136 | 0.080 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".