The movements of adult leerfish <i>Lichia amia</i> in the Breede Estuary, Western Cape, South Africa: insights from acoustic telemetry
Bibliographic record
Abstract
Leerfish Lichia amia inhabit estuaries as juveniles before moving between estuaries and the marine environment as sub-adults and adults. While adults remain mostly in the marine environment, philopatry has been observed, with individuals returning to areas previously used, including estuaries. The extent to which adults use estuaries, however, is underexplored. As such, this study aimed to investigate this species’ spatio-temporal use of the Breede Estuary in the Western Cape Province of South Africa, using a long-term acoustic telemetry dataset (from 2016–2019) for 8 sub-adult and 2 adult L. amia (690–890 mm fork length). On average, the tagged fish spent 16.6% of their days monitored in the estuary, predominantly in the lower reaches, and were also recorded along stretches of the coastline. Although the presence of L. amia was largely unaffected by month of the year, the highest monthly residency index (0.33) was recorded in May. Tagged fish were recorded entering the estuary on average at 14:29 (±01:46) and exiting on average at 09:33 (±00:54) the next day. Though L. amia appear to use estuaries less as they age, these habitats remain important even for adults, particularly before their spawning migration, emphasising the importance of maintaining the health of estuarine ecosystems for the species’ protection.
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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) | 0.002 | 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".