Study of climatological variability relationshipsrelated of the social variability season of freshwater fish landed in Rio GrandeRS
Bibliographic record
Abstract
In this study the south area of the State of Rio Grande do Sul was chosen, to study the possible relationship of climatological variables with the fish disembarkation, seeking to explore the possibility to include contributions of these varied in the models of administration of the stocks of fish of fresh water of the area included by the Pond of the Ducks and Little Pond. For this, it was used the data of fish disembarkation in the city of Rio Grande, during the years of 1987 and 2006 originating from of embarkations of the cities of São Lourenço do Sul, Pelotas, Rio Grande, São José do Norte and Santa Vitória do Palmar, systematically they are organized for IBAMA / CEPERG (Center of Research and Administration of the Fishing Resources Lagunares and Estuarinos ). The climatological data were obtained in the site http//www.cdc.noaa.gov/cdc (NOAA). Starting from the analysis of the data of fish disembarkation it was observed that the fish specimens Jundiá and Traíra act more of the half of all the captured production and disembarked in Porto of Rio Grande/RS city inside of the period study. It was calculated the averages trimestrais of disembarkation of fish of the specimens Jundiá and Traíra for the four quarters of every year. Starting from the analysis of the data, identified that the third quarter is the most significant in the requirement amount (kg) of disembarkation for the specimen jundiá , while for the specimen Traíra was the second quarter. Starting from the statistical analyses correlating fish disembarkation and climatological variables was verified that for the specimen Jundiá the more significant climatological variables for it quarter of larger importance were: for the month of component July V of the wind, rain and long wave radiation (LWR), for the month of August already, component U of the wind, temperature and (LWR) and, consequently, for the month of September the most significant variables are component U and V of the wind and speed of the wind. And for the specimen Traíra the climatological variables that obtained larger significance indexes for more expressive quarter were: for the month of component April V of the wind and temperature, for the month of May already, all the variables present important values for analysis, in other words, component variable U and V of the wind, speed, temperature, rain and long wave radiation (LWR) and, consequently, for the month of June the more significant climatological variables are component U of the wind, speed of the wind and rain.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".