The Canadian Genomic Adaptation and Resilience to Climate Change (GenARCC) Project
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Genomic technologies provide the highest resolution molecular information on species biology, and can help us understand risks and potential for adaptation among species in a changing environment. The Genomic Adaptation and Resilience to Climate Change (GenARCC) project uses molecular tools to identify ecosystem composition, pathogen and pest prevalence, and adaptive capacity within species. This Government of Canada project purposefully takes a multi-department/agency approach, drawing on complementary expertise and centralized infrastructure to address the complexity of climate change spanning multiple levels of trophic and taxonomic diversity. The GenARCC project’s goal is to develop capacity to use genomics to assess, predict, and adapt to climate change for the protection of Canada's biodiversity, ecosystem resilience, food security, and health. Together, the data and expertise generated in this project represent the largest single national effort and combined dataset to address climate change impacts across species and ecosystems with molecular data. GenARCC comprises research across a diversity of environments and species, covering forest and tundra, arctic, marine, and agricultural ecosystems. Genomic, climate and phenomic datasets have been generated and centralized to better understand impacts to biodiversity across biological levels of organisation. To facilitate storage and analysis of this data, we have deployed a common high performance compute environment allowing participants across multiple Canadian departments and agencies to leverage shared infrastructure, datasets and workflows. Project outputs and inputs are managed through a dedicated sample data research management platform, DINA (Fig. 1). DINA utilizes a highly flexible data model with a core set of fields that can be extended using domain-based standards as field-extensions and/or managed attributes that are user defined, with both able to leverage controlled vocabularies. Strong process-based provenance is maintained for samples and their derivatives. Samples managed, for example, range from individual specimens of bacteria and viruses, plants, insects, fish, and mammals to environmental samples of soil, water, and external and internal microbiomes. A robust API facilitates data migration and customized export supporting data analysis and publication (GitHub Repo). The project has produced several standardized pipelines using Snakemake and Nextflow for efficiency and ease of comparison. An effort was also made to standardize Canadian climatic data for modelling allowing for cross-species comparisons (Marquis 2024). Additionally, project participants benefit from training on scientific computing, as well as genomic methods in discussion forums for data analysis and integration best practices. This access to computing resources and training has supported publication of more than 30 studies that can be found on the GenARCC publication website. Results from this project will inform evidence-based policy to support conservation of biodiversity, as well as management of natural resources and key species across ecological realms.
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.
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 | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.012 | 0.004 |
| Scholarly communication | 0.001 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it