ZOOPLANKTON DISTRIBUTION FROM BACKSCATTER DATA OF ADCP INSTRUMENT IN WEST SUMATRA WATERS
Bibliographic record
Abstract
Acoustic Doppler Current Profiler (ADCP) conventionally used to monitor ocean current profiles and potentially detect zooplankton distribution remains largely unexplored. Zooplankton are key consumers in the marine food chain, therefore understanding their distribution is critical. This study aims to map the distribution of zooplankton in West Sumatra waters using ADCP backscatter data. Data analyzed encompass ocean current measurements, backscatter, and conductivity-temperature-depth (CTD) profiles collected from March 1 to 3, 2017. Raw ADCP digital counts were converted into mean volume backscattering strength (MVBS) in dB using sonar equations, proportional to zooplankton biomass. The conversion process involved corrections for sound attenuation due to distance and water absorption, ADCP transducer angle correction, and noise correction. Processing results revealed zooplankton distribution in raw ADCP data ranging from 20 to 160 counts and in MVBS data spanning -140 dB to -40 dB. MVBS values derived from ADCP acoustic signal processing were filtered within the -100 dB to -60 dB range, representing the zooplankton backscatter range. Zooplankton distribution was observed at depths of 0-300 m. Vertical zooplankton distribution was generally high in the 100-200 m layer and decreased at 0-100 m and 200-300 m. This is attributed to the influence of the Equatorial Undercurrent transporting zooplankton biomass from the Indian Ocean to West Sumatra waters at depths of 100-200 m, characterized by high salinity (34.6-35.2 PSU) and cold temperatures (19°-21°C). This study demonstrates the utility of ADCP in observing zooplankton distribution based on their backscatter values and the influence of ocean currents in transporting zooplankton biomass.
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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".