Spatial Data, Analysis and Modelling Forums: An initiative to broaden the collaborative research potential at DFO
Bibliographic record
Abstract
Science requires open, reproducible, and collaborative approaches to maximize efficiency and deliver improved outcomes. These proceedings summarize outcomes of the Spatial Data, Analysis and Modelling Forums organized as part of the R Learning and Development series by the Fisheries and Ocean Canada (DFO) Science Sector (Maritimes and Pacific Regions) in 2020. These forums and workshops enabled Science staff to: Exchange information about strategic planning and workflows to effectively organize data, code, and tools to support spatial analysis and modelling, Gain insight on how different data products could be, or have already been, used to inform spatial analysis and modelling, Share perspectives on the relevant considerations for using spatial data products and datasets, or the predictors in general, and any of the limitations users should be aware of, Learn about new spatial data products and existing platforms for sharing spatial data, Discuss the limitations of the available data products in terms of extent, resolution, quality and underlying assumptions. These proceedings provide an overall summary of this learning and development series, describing the materials, presentations, questions, and discussions. The main intent of this initiative was to provide a forum for DFO staff to present their ongoing work and issues in relation to spatial tools, data, analysis and modelling. A secondary goal was to learn how various programs and regions were resolving these issues and to build a common understanding of each other’s perspectives. This initiative was also conceived with the intent to foster collaborations, by helping DFO staff connect with colleagues that have shared challenges or interests. The information gleaned from discussions and participant surveys were used to guide and support future learning opportunities such as statistical modelling and programming language training. It is our hope that the information gathered from these learning and development opportunities can support already established working groups and task forces currently tackling data discovery and management challenges at DFO. Based on the presentations and discussions that followed during these events we present recommendations for increasing reproducibility and institutional efficiency in spatial analyses and modelling efforts.
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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 | 0.122 | 0.075 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.012 | 0.007 |
| Scholarly communication | 0.015 | 0.019 |
| Open science | 0.006 | 0.033 |
| Research integrity | 0.008 | 0.009 |
| Insufficient payload (model declined to judge) | 0.031 | 0.010 |
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".