Kejimkujik calibrated catchments: A benchmark dataset for long‐term impacts of terrestrial and freshwater acidification
Bibliographic record
Abstract
Abstract Delays in forest recovery from terrestrial acidification combined with climate change are leading Acadian Forest ecosystems into new territory. The Kejimkujik Calibrated Catchments (KCC) Study Program was established in and adjacent to Kejimkujik National Park and Historic Site (KNP) in Southwest Nova Scotia (SWNS), Canada, in the late 1970s to study the impacts of acid precipitation on pristine and vulnerable ecosystems. The 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 with regular water chemistry sampling, 12 forest inventory plots, and an atmospheric deposition monitoring station. Recently, LiDAR data coverage was acquired for the study basins. Data collected at the KCC form part of the lake monitoring program of Environment and Climate Change Canada used in acidification, climate change, and mercury studies. Important characteristics of the KCC watersheds are their high sensitivity to acid deposition, high and increasing dissolved organic carbon (DOC) levels and lowland topography causing extensive wetlands. The KCC are also emerging as an important location for the study and protection of terrestrial and aquatic species‐at‐risk.
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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 | 0.000 |
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".