Zooplankton size spectra and production assessed by two different nets in the subarctic Northeast Pacific
Bibliographic record
Abstract
Abstract Normalized biomass size spectra (NBSS) are frequently used to describe pelagic communities. However, the underlying structure of NBSS may lead to varying intercepts and slopes when only a portion of the biomass range is sampled. This may be further perpetuated by the sampling efficiency of different gears/mesh sizes. Spatial and seasonal effects of mesh size on zooplankton NBSS and production were evaluated. Zooplankton were collected during winter, spring and summer (2017–2019) between Vancouver Island and Station Papa (50°N, 145°W) using a 64-μm Working Party 2 (WP-2) net and a 236-μm bongo net and analyzed using a bench-top laser optic particle counter. WP-2 and bongo NBSS overlapped in 11 size classes, for which the WP-2 more effectively sampled smaller size classes and converged with the bongo in larger size classes. Differences in NBSS slopes from the two nets were detected, yet no differences in total production. However, the contribution of individual size classes to total production varied spatially and seasonally. Total production in the coastal region exhibited strong seasonal variability. Notably, summer estimates of production in the coastal region were at least 2-fold higher than transitional and open ocean regions. This study suggests that using one mesh size may underestimate zooplankton NBSS and thus production.
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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".