The Haber Bosch–harmful algal bloom (HB–HAB) link
Bibliographic record
Abstract
Large-scale commercialization of the Haber–Bosch (HB) process is resulting in intensification of nitrogen (N) fertilizer use worldwide. Globally N fertilizer use is far outpacing that of phosphorus (P) fertilizer. Much of the increase in N fertilizers is also now in the form of urea, a reduced form of N. Incorporation of these fertilizers into agricultural products is inefficient leading to significant environmental pollution and aquatic eutrophication. Of particular concern is the increased occurrence of harmful algal blooms (HABs) in waters receiving nutrient enriched runoff. Many phytoplankton causing HABs have physiological adaptive strategies that make them favored under conditions of elevated N : P conditions and supply of chemically reduced N (ammonium, urea). We propose that the HB-HAB link is a function of (1) the inefficiency of incorporation of N fertilizers in the food supply chain, the leakiness of the N cycle from crop to table, and the fate of lost N relative to P to the environment; and (2) adaptive physiology of many HABs to thrive in environments in which there is excess N relative to classic nutrient stoichiometric proportions and where chemically reduced forms of N dominate. The rate of HAB expansion is particularly pronounced in China where N fertilizer use has escalated very rapidly, where soil retention is declining, and where blooms have had large economic and ecological impacts. There, in addition to increased use of urea and high N : P based fertilizers overall, escalating aquaculture production adds to the availability of reduced forms of N, as does atmospheric deposition of ammonia. HABs in both freshwaters and marginal seas in China are highly related to these overall changing N loads and ratios. Without more aggressive N control the future outlook in terms of HABs is likely to include more events, more often, and they may also be more toxic.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.026 | 0.005 |
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".