Length-Weight Relationships and Other Morphological Traits of Fishes in the Mangrove of Hainan, China
Bibliographic record
Abstract
The length–weight relationships (LWR) and other morphological traits for 6417 specimens in 74 fish species collected seasonally, from July 2020 to April 2021, in the mangrove of Dongzhaigang Bay, Hainan Province, China, are presented. This involved, for all species, a sample size, and minimum and maximum lengths; in addition, for most species, it involved linear relationships between the standard length and body height and width, the height of the caudal peduncle, the pectoral fin length, and the eye diameter. This extensive coverage of mangrove-resident fish species (as opposed to species using the mangrove only as a nursery) is a first for Hainan. The various morphological traits of the mangrove fish are, for several species, the first to be published since these species’ original descriptions.
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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) | 0.000 | 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) | 0.001 | 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".