Kejimkujik Calibrated Catchments: a benchmark dataset for long-term impacts of terrestrial acidification
Bibliographic record
Abstract
Delays in forest recovery from terrestrial acidification combined with climate change is leading Acadian Forest ecosystems into new territory. Kejimkujik Calibrated Catchments (KCC) Study Program was established in an around Kejimkujik National Park and Historic Site (KNPHS) in Southwest Nova Scotia (SWNS) in the late 1970s to increase our understanding of the impacts of acid precipitation on relatively pristine ecosystems. KCC now have one of the longest continuously monitored water chemistry records in North America, with data collection beginning in 1980. Its infrastructure includes three gauged streams, twelve forest inventory plots, an atmospheric deposition monitoring station, and three streams with continuous water quality monitoring and regular lab analysis of stream chemistry, and recent LiDAR coverage. The KCC fits into a wider network of monitored lakes. Data collected at the KCC form a key datapoint in comparisons of catchment response to terrestrial acidification in the context of a warming climate, due to their high and increasing DOC levels, highly dilute waters, lowland topography and extensive wetlands. KCC are also emerging as an important source of information for species at risk protection as SWNS was declared one of the 11 national priority places for biodiversity protection.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".