Threat assessment for Pacific sand lance (Ammodytes personatus) in the Salish Sea
Bibliographic record
Abstract
Like many forage fish species, Pacific sand lance ( Ammodytes personatus ) play a key role in nearshore marine ecosystems as an important prey source for a diverse array of predators in the northeastern Pacific. However, the primary threats to Pacific sand lance and their habitat are poorly defined due to a lack of systematic data. Crucial information needed to assess their population status is also lacking including basic knowledge of their local and regional abundance and distribution. Sand lance are currently listed as ‘not evaluated’ under the IUCN red list and they have not been assessed by US and Canadian agencies. This hampers management and policy efforts focused on their conservation. To address this knowledge gap, we conducted a three-part, structured expert elicitation to assess the vulnerability of Salish Sea sand lance populations. Experts were asked to list and rank key threats to Salish Sea sand lance and/or their habitat, to further quantify the vulnerability of sand lance to identified threats using a vulnerability matrix, and to predict the population trajectory in 25 years from today. Impacts associated with climate change (e.g. sea level rise, sea temperature rise, ocean acidification, and extreme weather) consistently ranked high as threats of concern in the ranking exercise and quantified vulnerability scores. Nearly every expert predicted the population will have declined from current levels in 25 years. These results suggest sand lance face numerous threats and may be in decline under current conditions. This research provides vital information about which threats pose the greatest risk to the long-term health of sand lance populations and their habitat. Managers can use this information to prioritize which threats to address. Future research to reliably quantify population size, better understand the roles of natural and anthropogenic impacts, and to identify the most cost-effective actions to mitigate multiple threats, is recommended.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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 teacher head, 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".