Data from: Effects of changing ocean temperatures on ecological connectivity among marine protected areas in northern British Columbia
Bibliographic record
Abstract
This repository contains spatial layers from the analysis in Friesen et al (2021). See "Related Publication". Our objective was to assess how ecological connectivity between MPAs may shift with projected seasonal ocean temperature changes in the Northern Shelf Bioregion in British Columbia, Canada. We used benthic temperature outputs from a regional ocean model of the British Columbia continental margin. These model outputs cover a hindcast simulation (1981-2010) and a future climate projection based on the RCP8.5 scenario (2041-2070). To generate the input layers for the analysis, we first calculated mean seasonal temperatures for each pixel in the two time periods. We also subtracted the hindcast temperature value from the projected future temperature value to determine temperature change, then averaged temperature change across the pixels intersecting each MPA. Second, we modelled suitable habitats for each time period and season based on the adult environmental preferences of two case study species: Metacarcinus magister (Dungeness Crab) and Sebastolobus alascanus (Shortspine Thornyhead). Third, we generated resistance surfaces for each time period and season using the adult environmental preferences of the two case study species. Fourth, we identified MPAs that contained suitable habitats and defined those as network nodes for the connectivity analyses. Once the analysis input layers were generated, we applied least-cost and circuit theory-based tools to identify potential linkages between MPAs via adult movement and compare MPA interconnectedness between the two time periods. Specifically, we used Linkage Mapper to generate least-cost corridors and Pinchpoint Mapper to conduct a seascape-level analysis simulating random exploratory movements within the network. This repository contains the layers from each step of this analysis.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.045 | 0.008 |
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".