Bibliographic record
Abstract
Without nitrogen (N) and phosphorus (P) there would be no life – no me, no you, no rainbow trout, no Atlantic cod, no Antarctic krill. But like anything in excess, too much N and P leads to a series of negative consequences. For example, in aquatic systems, high nutrient levels can lead to harmful algal blooms, low dissolved oxygen events, and decreases in biodiversity. Despite these well-known adverse impacts, no US states to date have implemented Environmental Protection Agency-approved N and P criteria for all water bodies within their respective borders (although Hawaii, it should be noted, has N and P standards for all water bodies except wetlands). Citing concerns on the part of stakeholders (citizens, industry, and lobbying groups), many state lawmakers are hesitant to adopt numeric nutrient criteria to regulate N and P, despite the 40-year-old charge in the US Clean Water Act. In some proactive US states, natural resource managers informed by the scientific literature – most notably the whole-system experimental manipulations in Canadian lakes – established P management strategies that resulted in water-quality goals being met. However, because P is likely not the limiting nutrient in most marine systems, P mitigation strategies did little to improve downstream coastal water quality. Many ecosystem ecologists therefore argue for reductions in both N and P. Nevertheless, the contention exists in the regulation community that the evidence for dual nutrient reduction is simply not compelling enough and that the cost is too high to justify. So how do we continue a rigorous scientific debate while also providing an unambiguous, action-oriented message to non-scientist stakeholders? First, researchers can undertake due diligence by clearly presenting to stakeholders and policy makers the basic and staggering effects that humans have had on N and P cycling. Most people are well aware of the impact of anthropogenic increases in carbon dioxide, yet humans have arguably done more to influence N and P cycling globally. For example, since the beginning of the 20th century, human activities have doubled the amount of N cycling through the biosphere and tripled the amount of P mobilized on Earth. In doing so, we have fundamentally altered terrestrial and aquatic ecosystem functioning. Now, with more than 50% and 90% of the world's population living within 3 km and 10 km of freshwater sources, respectively, human impacts on aquatic resources worldwide are enormous and growing. Second, given the longstanding and vigorous debate among policy makers over whether to manage N or P or both, scientists can advocate for a holistic, watershed-level, dual-nutrient approach. Unfortunately, in some instances, researchers inadvertently confuse the issue with academic debates over whether N or P is the nutrient limiting growth in a given terrestrial or aquatic system. Although intellectually important, these debates – outside of academia – sometimes obfuscate the need for dual-nutrient policy and provide policy makers with a justification to limit either N or P but not both. Worse yet, as we have seen recently in the climate-change debate, the lack of clear public communication of the state of the science has led to instances of researchers appearing “unsure” of their findings and, as a result, efforts to establish relevant policy have been abandoned. Third – and borrowing from Voltaire – remember that perfect is the enemy of good. In the case of water-quality standards, it is safe to say that the arsenal of scientific literature and sentiment strongly supports criteria for both N and P, a refrain that should be repeated often. Even if it does have criteria for both N and P, the final policy, while not perfect, will serve the greater good in protecting waters both locally and downstream. “Primum non nocere”, or “first, do no harm”, is one of the central ethical principles taught to medical students, and one that scientists and policy makers alike should embrace. In this context, to do no harm, we argue that a systems-wide, holistic approach to N and P control is essential. When developing nutrient criteria for a given river or estuary, it is imperative for policy makers to remember that actions in one watershed will affect a neighboring or downstream ecosystem. Since the passage of the Clean Water Act 40 years ago, the road to comprehensive nutrient pollution policy has been slow and sporadic. Yet the need for action has never been greater, and the impact that scientists can make on policy should not be underestimated.
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 | 0.010 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.015 |
| Scholarly communication | 0.012 | 0.015 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.013 | 0.015 |
| Insufficient payload (model declined to judge) | 0.083 | 0.059 |
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".