Data and information management for the monitoring of biodiversity in Alberta
Bibliographic record
Abstract
ABSTRACT The Alberta Biodiversity Monitoring Institute (ABMI) monitors large‐scale responses of biodiversity to environmental change in Alberta, Canada, based on standardized and ongoing data collection. In this case study, we show that such a standardized monitoring system has many data management challenges. In the almost 20 years since the conception of the Institute, we have identified 4 key characteristics that are required for large‐scale, long‐term biodiversity monitoring programs to be operational: 1) data must be publicly accessible; 2) methods and terminology must be standardized to facilitate consistency around data and information collection, analysis, and reporting; 3) the information system must be flexible so that components can be modified or added without compromising the functionality of the other components or the whole system; and 4) the system must be scalable so that it can support input, storage, and retrieval as data load increases. These characteristics are important to ensure that the products and tools generated from our monitoring program can support management at large spatial scales in the complex socio‐ecological system of Alberta. © 2015 The Wildlife Society.
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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 | 0.010 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.007 | 0.012 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".