Comparison of mesozooplankton production estimates from Saanich Inlet (British Columbia, Canada) using the chitobiase and biomass size spectra approaches
Bibliographic record
Abstract
Zooplankton production estimates are necessary to understand the availability and transfer of energy to higher trophic levels in marine food webs. Methods have been developed to quantify zooplankton production; however, they are difficult to compare as they focus on single species, groups, stages, or size classes of zooplankton. We compared 2 methods for estimating crustacean production: the chitobiase method (based on a crustacean moulting enzyme), and 3 empirical growth rate models (Huntley-Lopez, Hirst-Lampitt, and Hirst-Bunker) applied to optically resolved mesozooplankton normalized biomass size spectra (NBSS). Mesozooplankton net samples were collected between March and August of 2010 and 2011 in Saanich Inlet (British Columbia, Canada) and analyzed in the laboratory using microscopy and a bench-top laser optical particle counter (lab-LOPC). Microscope and lab-LOPC estimates of abundance and biomass were in close agreement. Crustacean production estimates were highest using Huntley-Lopez (0.20-185.3 mg C m-3 d-1), followed by Hirst-Bunker (0 .01-18.3 mg C m-3 d-1), chitobiase (0.05-15.6 mg C m-3 d-1), and Hirst-Lampitt (0.03-14.3 mg C m-3 d-1). Hirst-Lampitt-, Hirst-Bunker-, and chitobiase-based estimates of crustacean production and trophic transfer efficiency (TTE) yielded similar patterns/magnitude, while the Huntley-Lopez model was more variable. Estimates showed stronger agreement in 2011 than in 2010, attributed to the shift from El Niño to La Niña conditions. We highlight similarities/differences associated with these techniques and suggest that Hirst-Bunker estimates of production and TTE are most consistent with chitobiase-based values.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".