Bibliographic record
Abstract
This paper proposes a new nitrogen-based trophic state index (TSI) for the estimation of status of eutrophication in a lagoon system. Nitrite-nitrogen (NO2-N) is preferable because of its greater abundance in Chilika lagoon and its relation to other criteria of trophic state, for example, chlorophyll-a (Chl-a) and Secchi disk depth (SDD). Nitrite is preferable over nitrate because the former decreases the fluorescence and affects photosynthesis, thereby controlling primary production. This paper also computes TSI using Chl-a and SDD. The three parameters account for the biological, chemical, and physical characteristics of the lagoon. It will be possible to estimate the TSI of freshwater and brackish water lagoons and other water bodies using the new expressions taking into consideration the spatial and temporal variability in the dataset. Depending on the data availability, alternative TSI (Chl-a) and TSI (SDD) can account for the biological and physical contributions to eutrophication. The estimated TSI can account for Chl-a and NO2-N up to 322.18 mg m−3 and 61.99 μg L−1, respectively. The TSI based on these three parameters can serve as a complimentary and predictive tool for lagoon management and field programs to monitor the health of a lagoon.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".