An Assessment of Seasonal Differences in Fish Populations in Laizhou Bay Using Environmental DNA and Conventional Resource Survey Techniques
Bibliographic record
Abstract
In recent years, environmental DNA (eDNA) technology has gradually improved, and it has been increasingly used to monitor marine fish. The decline and seasonal fluctuations of fish resources in Laizhou Bay, Bohai were studied using eDNA technology and compared with the results of conventional fish resource survey methods. In November 2020 (autumn), March 2021 (spring), and July 2021 (summer), 12 samples were collected each quarter in Laizhou Bay and adjacent waters for a total of 36 eDNA samples, and 47 fish species were identified. During the same trip, ground cages, gillnets, and trawls were used during two seasons. Fishery resource surveys were conducted at 12 sites from November 2020 (autumn) to March 2021 (spring), and in total 11 fish species were found. Our study found that fishery resources in Laizhou Bay significantly fluctuated with seasonal changes. Additionally, compared with traditional surveys, eDNA information included the same results, but also included fish that could not be collected because of the technical limitations of traditional surveys. Therefore, this study provides more accurate seasonal information for fish in Laizhou Bay, which is of great significance for the long-term management and conservation of coastal biodiversity.
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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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".