The Application of Fish Scales in Removing Heavy, Metals from Energy-Produced Waste Streams: The Role of Microbes
Bibliographic record
Abstract
In energy production, heavy metals pose significant contamination hazards. For example, the petroleum industry generates wastes that are often high in heavy metal concentrations. Heavy metals are very toxic and extremely deleterious to humans, plants, and animals. Application of fish scale to remove heavy metals is a very recent innovation. It is an environmentally appealing and economically attractive alternative to current heavy metal adsorbing materials. Previously, the adsorption phenomenon on this exotic waste material was explained by only physical-chemical reactions. Biological effects on adsorption of heavy metals such as lead, arsenic, and chromium were studied using Atlantic Cod scale. The difference in results between nonsterilized and sterilized experiments shows the microbial contribution to heavy metal removal. Results show a wide range of microbial contribution in removing chromium cations. For lead and arsenic cations, the effect is less. Measurement of pH gives some indication of the microbial role in the biosorption process and of the presence of possible microbial species.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".