Quantifying Benthic Flux of Mysis Biomass Through Diel Vertical Migration at the Ecosystem Scale
Bibliographic record
Abstract
Abstract Mysis diluviana is a macroinvertebrate that couples benthic and pelagic habitats on a daily timescale through diel vertical migration (DVM). However, quantifying how much Mysis biomass is exchanged between benthic and pelagic habitats at an ecosystem scale is difficult because of sampling limitations and variability in Mysis DVM behavior related to light and depth. Although Mysis are benthic-pelagic migrators, a portion remains pelagic during the day offshore in Lake Ontario, partially contradicting the assumption of population-level DVM over deep areas. To estimate the amount of biomass transferred from benthic to pelagic habitat via DVM in Lake Ontario, we estimated the portion of pelagic biomass at night originating from benthic habitat as the difference between night and day pelagic estimates from net tows along a bathymetric depth gradient. We then modeled the portion as a function of depth, extrapolated these depth-dependent estimates to an existing lake wide night-pelagic dataset, and summed amounts across depth strata. We estimated more biomass was transferred from benthic to pelagic habitat at intermediate lake depths (100–160 m) despite greater offshore (> 180 m) night-pelagic biomass. Our results suggest ways to improve estimates of Mysis habitat coupling and how to account for important factors such as depth and light for modeling Mysis DVM behavior at the population- and ecosystem-levels.
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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 | 0.000 |
| 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".