A feasibility study for the application of climate change vulnerability assessments on species in the <scp>Tallurutiup Imanga National Marine Conservation Area</scp>
Bibliographic record
Abstract
Abstract Climate change is disproportionately affecting Arctic ecosystems and their resident species, but knowledge gaps complicate conservation planning. A proof‐of‐concept application of existing trait‐based vulnerability assessment frameworks were applied to nine species from three different taxa (cetaceans, pinnipeds, marine fish) to determine their vulnerability under the RCP 8.5 emissions scenario. A literature review and data gap analysis were performed to quantify vulnerability and recommend future research priorities. Each assessed species was found to be highly vulnerable to climate change, with char, cod, and walrus being the most vulnerable. The largest data gaps include outdated or missing abundance measurements mortality rates in both marine mammals and fish, and reproductive behaviours for fish specifically. Future assessments should consider multiple emissions scenarios that match the ranges of migratory species not confined to the TINMCA. Providing a method to preliminarily evaluate climate change vulnerability may help mitigate issues brought by limited species experts, such as respondent fatigue. More refined scores will require the assistance of species experts, including biologists and Indigenous knowledge holders.
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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 | 0.014 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".