Developing Conceptual Frameworks for the Recovery of Aquatic Biota from Acidification
Bibliographic record
Abstract
Surface water acidity is decreasing in large areas of Europe and North America in response to reductions in atmospheric S deposition, but the ecological responses to these water-quality improvements are uncertain. Biota are recovering in some lakes and rivers, as water quality improves, but they are not yet recovering in others. To make sense of these different responses, and to foster effective management of the acid rain problem, we need to understand 2 things: i) the sequence of ecological steps needed for biotic communities to recover; and ii) where and how to intervene in this process should recovery stall. Here our purpose is to develop conceptual frameworks to serve these 2 needs. In the first framework, the primarily ecological one, a decision tree highlights the sequence of processes necessary for ecological recovery, linking them with management tools and responses to bottlenecks in the process. These bottlenecks are inadequate water quality, an inadequate supply of colonists to permit establishment, and community-level impediments to recovery dynamics. A second, more management-oriented framework identifies where we can intervene to overcome these bottlenecks, and what research is needed to build the models to operationalize the framework. Our ability to assess the benefits of S emission reduction would be simplified if we had models to predict the rate and extent of ecological recovery from acidification. To build such models we must identify the ecological steps in the recovery process. The frameworks we present will advance us towards this goal.
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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 | 0.013 | 0.010 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.003 | 0.014 |
| Scholarly communication | 0.008 | 0.011 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".