Use of Toxicity Identification Evaluation Procedures to Clarify the Relationship Between Ammonium Concentrations and Phytoplankton Blooms in the San Francisco Bay Estuary, California, USA
Bibliographic record
Abstract
Phytoplankton blooms in the northern San Francisco Bay Estuary have historically supported much of the larval fish production in the estuary. In the past, blooms were limited largely by reduced light intensities and net outflows through the system, as well as dense populations of introduced clams that continuously filter the water column. Conversely, the estuary is exposed to a wide variety of contaminants that may also impact phytoplankton growth. Interestingly, previous investigations have suggested that relatively low concentrations of ammonium may inhibit development of bloom conditions by interfering with nitrate assimilation. Given the complex dynamics of the system, with multiple factors that could potentially affect algal growth, additional data to validate this hypothesis are important to identify appropriate management options. Consequently, toxicity identification evaluation (TIE) procedures were applied to ambient water samples and monitored for 72-96 h under controlled conditions to evaluate their effects on algal growth and utilization of dissolved inorganic nitrogen. The TIE treatments specifically targeted ammonium, as well as the potential contributions of metals and nonpolar organic contaminants. Notably, all samples exhibited positive growth over the exposure period with no evidence of toxicity, and TIE treatments did not further improve growth. A subsequent 72-h study evaluated the effect of ammonium up to 12 µM at a fixed concentration of nitrate was monitored at 24-h intervals and showed no inhibition of the development of bloom conditions. Collectively, there was no evidence that ammonium interfered with growth, even at concentrations well above the range of postulated effect levels. Of additional interest, the lack of increased growth in TIE treatments targeting chelatable metals and nonpolar organics suggested that these contaminant classes were not present at inhibitory concentrations. These results demonstrate the importance of validation of cause in multistressor environments, and further clarify the roles of different factors that may limit development of bloom conditions in the estuary. Environ Toxicol Chem 2023;42:178-190. © 2022 SETAC.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".