Documenting the impacts of increasing salinity in freshwater and coastal ecosystems: Introduction to the special issue
Bibliographic record
Abstract
Freshwater salinization is the process of changing ion concentrations (e.g., Na+, Mg2+, K+, Cl−, , ) relative to background levels due to human activities (e.g., agriculture, application of road de-icing salts, water and resource extraction, climate change, and sea-level rise; Williams 2001; Cañedo-Argüelles et al. 2016). Although considerably less studied than other environmental issues (Cañedo-Argüelles 2020), salinization is widely accepted as presenting major challenges to freshwater and coastal biodiversity (Cunillera-Montcusí et al. 2022). Existing data and research show a clear rise in freshwater salinization worldwide (Dugan et al. 2017; Cañedo-Argüelles 2020; Jeppesen et al. 2020; Kaushal et al. 2021), yet key knowledge gaps and management challenges remain due to the complexity (Kaushal et al. 2018) and prevalence of the problem (Cañedo-Argüelles 2020; Jeppesen et al. 2020). Current literature has neglected to provide unbiased geographic coverage (Cunillera-Montcusí et al. 2022), and ecosystem-level responses (e.g., functions and services) are rarely assessed (Herbert et al. 2015). Compelling calls for research agendas that address the need for salinization research at multiple scales (e.g., global, regional, local) are well timed (Cunillera-Montcusí et al. 2022). One identified research gap points to the need for networks of researchers working together at regional scales using experimental approaches to identify impacts on biodiversity, community salinity thresholds, and landscape-scale drivers. Here, we document the results of a networked Global Salt Initiative (GSI) that performed in situ experiments on lakes to look at ecosystem-level impacts: their findings suggest that North American and European water quality guidelines for salt are far too low to prevent ecosystem-level impacts. The further purpose of this Special Issue (SI) is to document the results of ecosystem-level impacts of increasing salinity on lake and coastal area biodiversity and ecosystem functioning from a variety of perspectives.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.020 | 0.008 |
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".