Monitoring Cumulative Effects of Human Activity on Alberta’s (Canada) Biodiversity
Bibliographic record
Abstract
stematic grid of 1,656 site locations across the province, spaced 20 km apart, to collect biodiversity information on terrestrial and wetland sites. At each location, data and field samples are collected for a wide range of plant and animal species through on-the-ground measurements, and also using motion-sensitive camera traps and acoustic technology. Since 2007, over 480,000 specimens-data on over 3000 species have been collected and processed, many of which represent new scientific records for the province, sometimes new records for Canada, and even records new to science. Annually, a percentage of the total sites is surveyed, with the sites revisited approximately every 7 years to measure trend in species abundance. In addition to field surveys, Alberta's land cover and human footprint is monitored using remote sensing technology at two spatial scales. To report on patterns and trend in human footprint, the ABMI classifies human footprint into 115 feature types, which are then rolled up into the categories of energy, forestry, agriculture, residential and industrial, human-created water bodies, and transportation. The ABMI's accumulated biodiversity and HF database supports the creation of predictive species models that provide information on spatial distribution, habitat associations, responses to HF, and predicted relative abundance for over 800 species, including mammals, birds, soil mites, vascular plants, mosses (bryophytes), and lichens. The scale and depth of the ABMI's monitoring program and biodiversity data make it a unique program nationally, and a leader internationally. In addition to ongoing protocol development and data analysis, the ABMI is committed to deriving value from its data and information for a wide range of Alberta stakeholders through concerted knowledge translation and stakeholder engagement 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 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) | 0.001 | 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 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".