IMPACTOS PROVOCADOS PELO DESCARTE DO Limnoperna fortunei EM PISCICULTURAS DO SUB-MÉDIO RIO SÃO FRANCISCO
Bibliographic record
Abstract
In order to verify the impacts, final disposal and disposal of shells removed in the cleaning of cultivation structures in six fish farms in Jatobá-PE were observed in loco the forms of disposal that are performed by fish farmers also as well the due impacts from the shells of the golden mussel Limnoperna fortunei (Dunker, 1856).These observations it happen in two distinct periods dry and rainy periods of 2018.The Jatobá-PE fish farmers ended up suffering economic impacts due to the increase in investment that was required to repair the structures that suffered damage in the act of removing the encrusted shells which may also cause impacts to the which may also cause environmental impacts if the due proper final disposal not happen it.Given the observations it was found that fish farmers use different methodologies at the time of disposal, which may or may not impact the environment that these shells are discarded.Given the importance of aquaculture production in the region and the impacts that it itself can suffer due to the unwanted presence of the golden mussel, harming the species to be cultivated.With this in mind, work on this theme is important to facilitate the resolution of problems related to the invasive mussel that other producers face in their daily lives, causing a decrease in their production.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.016 |
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; both teacher heads 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".