RICHNESS OF COMMON NAMES OF BRAZILIAN MARINE FISHES AND ITS EFFECT ON CATCH STATISTICS
Bibliographic record
Abstract
The richness of common names of Brazilian marine fishes was studied based on a sample of 725 species, covering 67% of all marine fishes recorded in Brazil. The richness of names is considerable (mean = six common names per species) and is positively related to commercial importance and habitat type, with more names associated with exploited or reef-associated, pelagic, and demersal species. No names were associated with bathypelagic, bathydemersal, and benthopelagic fishes. This richness, while culturally and linguistically interesting, poses a problem for national catch statistics. Some species such as Aspistor quadriscutis, Cathorops spixii, and Genidens genidens were not listed in the catch statistics, but may have been caught for a long time without being recorded. Catches of Sardinella brasiliensis may be higher than what was officially reported, only due to the use of different common names. This may contribute to slow down the recovery process of this collapsed stock. Any attempt to assess the relative impact of different fishing sectors (subsistence, artisanal, industrial, and recreational) on the ecosystem will be undermined by the incomplete understanding of the connection between folk and scientific nomenclature. This issue is even more pervasive when each sector uses its own common name to describe the same 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 | 0.003 | 0.035 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".