A novel design for sampling benthic zooplankton communities in disparate Gulf of Alaska habitats using an autonomous deep-water plankton pump
Bibliographic record
Abstract
Abstract Deep-water larval fish and zooplankton utilize structurally complex, cold-water coral and sponge (CWCS) habitats as refuges, nurseries and feeding grounds. Fine-scale sampling of these habitats for larval fish and zooplankton has proven difficult. This study implemented a newly designed, autonomous, noninvasive plankton pump sampler that collected large mesozooplankton within 1 m of the seafloor. It was successfully deployed in the western Gulf of Alaska between the Shumagin Islands (~158°W) and Samalga Pass (−170°W), and collected in situ zooplankton from diverse benthic communities (coral, sponge and bare substrates) at depths in excess of 100 m. Key design parameters of the plankton pump were its ability to be deployed from ships of opportunity, be untethered from the vessel during sampling and be deployed and retrieved in high-relief, rocky areas where CWCS are typically present. The plankton pump remains stationary while collecting from the water column, rests within 1 m of the seafloor and captures images of the surrounding habitat and substrate. This plankton pump design is a low-cost, highly portable solution for assessing the role of benthic habitat in the life cycle of mesozooplankton, a linkage that has been relatively underexplored due to the difficulty in obtaining near-bottom samples.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".