Bibliographic record
Abstract
Vanadium, a relatively abundant heavy metal, enters the environment naturally through rock weathering. A large fraction of vanadium input is of human origin. The combustion of petroleum- and coal-products, which contain relatively high concentrations of vanadium, is one of the most important sources of the enrichment of vanadium in the environment. As it is used as an alloy, and vanadium rich iron-ores of various origin are used in steel production, the residual slag-stones of the steel industry can contain considerable vanadium concentrations. Wherever slag-stones serve as a cheap and convenient material in riverbank reinforcement, vanadium can leach into the aquatic environment. Vanadium is regarded as an essential trace element for higher animals. Cantley et al. indicated a regulatory function of vanadate in vivo. Although considerable information is available on the toxic effects of vanadium on humans, very little is known about the toxicity of vanadium towards aquatic organisms, especially invertebrates. Bell and Sargent have shown an inhibition of Na-K-ATPase activity in gills of the eel Anguilla anguilla. Holleland and Towle have demonstrated the inhibition of Na-K-ATPase activity in the gills of the shore crab Carcinus maenas. The aim of this study was to determine the toxicity of vanadium towards themore » brackish water hydroid Cordylophora caspia. Hydroids are known to be particularly sensitive to heavy metals and their asexual reproduction can be used in a well-established population growth test. Furthermore, the effects of vanadium on Na-K-ATPase activity in hydroids were studied in in vivo experiments, wherein the animals were exposed to sublethal concentrations of vanadium. In addition, the inhibition of Na-K-ATPase was measured in vitro, by adding vanadium to a microsomal preparation. 16 refs., 4 figs.« less
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.004 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.006 | 0.005 |
| Research integrity | 0.008 | 0.004 |
| Insufficient payload (model declined to judge) | 0.990 | 0.993 |
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".