Bibliographic record
Abstract
Nitrogen nutrients and nickel(Ni) are ubiquitous in aquatic environments, and they are important for primary production of ocean ecosystem. This study examined the interaction of nitrogen nutrients (specifically urea and nitrate) and Ni on chlorophyll (Chl a) concentration and photosynthesis parameters values of Prorocentrum donghaiense and Skeletonema costatum. The data presented here indicate that low concentration of Ni for P. donghaiense and S. costatum can enhance both Chl a concentration and photosynthesis parameters values when grown in urea containing environment. Despite this increase there was also an observed depression in both species tested when incubated in high concentration of Ni for P. donghaiense and S. costatum regardless of incubating in urea or nitrate. Additionally, EC50 values of Chl a and Fv/Fm for Ni at different time intervals were calculated in this study. These observations indicated that the Ni tolerance was higher in P. donghaiense as compared to S. costatum. The Ni tolerance of P. donghaiense incubated in urea was higher than that incubating in nitrate. The same phenomenon was not observed in S. costatum, which indicated that the influence of urea was dependent on the species investigated. Thus, urea input could impact Ni bioavailability and toxicity, and then affect the biodynamics thereafter.
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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) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.391 | 0.467 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".