Headwaters to oceans: Ecological and biogeochemical contrasts across the aquatic continuum
Bibliographic record
Abstract
Abstract While the disciplines of oceanography and limnology often operate in isolate, freshwater, estuarine, and marine ecosystems are intricately linked. The emphasis of this special issue for Limnology and Oceanography is on the aquatic continuum and the connectivity between aquatic ecosystems from headwater streams and inland waters, to coastal and marine systems. Changes in the transport and transformation of elements as well as ecological functions occur along this aquatic continuum. Assemblages of organisms change in a way that reflects the ecological and biogeochemical conditions of the aquatic gradient. Here, we highlight research progress in limnology and oceanography across the aquatic continuum and at the interfaces of headwaters to oceans. Contributions explored nutrient and carbon dynamics which included release, transportation, transformation, and stoichiometry from freshwaters to marine. The special issue also explored food web continua, including functional changes, biodiversity gradients, and photosynthesis and respiration comparisons among ecosystems at different points in the continuum. Rapid improvements in biomolecular techniques, use of long‐term datasets, applications of novel statistical methods, and improved upscaling methods can transform the way aquatic scientists are describing biological organisms and communities from freshwaters to oceans. One important conclusion is the recognition that anthropogenic activities such as invasive species and nutrient pollution trigger challenge the current concepts of aquatic continua including the river continuum concept, the land to ocean continuum, river to estuary systems, and the submarine groundwater discharge. Both limnologists and oceanographers have much to gain from exchanging information with one another, especially in light of global change.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".