Future Habitat Working Group: Summary Report
Bibliographic record
Abstract
In 2020 scientists at Fisheries and Oceans Canada (DFO) formed a transdisciplinary working group to develop methodologies for estimating the effects of climate change on marine organisms and ecosystems in North-eastern Pacific coastal waters. This collaborative network was initially designed for the discussion of relevant projects and issues and meets annually. A subset of that group formed a Technical Working Group (TWG) that implemented a convergence research approach to developing a methodology for Species Distribution Modelling under Climate Change. The group consisted of multi-disciplinary experts from DFO Pacific Region and met bi-weekly. This document summarizes the working group activities including the structure and history of the group, products and an overview of the lessons learned. The group arrived at several key outcomes: 1. environmental monitoring data should be collected concurrently with species sampling across a wide range of environmental conditions with calibrated instruments and standardized sampling protocols; 2. regional ocean downscaling of climate projections should match the species spatial scale (at a resolution fine enough to resolve nearshore areas where many species live) and that further collaborative research is needed to identify regions where we have sufficient species data, identify the resolution needed given the spatial extent of the data and, develop and run dynamical models; 3. tools for statistical downscaling are needed to reduce climate projection uncertainties.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.106 | 0.064 |
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".