Evaluation of chitobiase-based estimates of biomass and production rates for developing freshwater crustacean zooplankton communities
Bibliographic record
Abstract
Increasing attention has been devoted to the development of alternative (often biochemical) methods for measuring crustacean zooplankton productivity because conventional methods are not globally applicable and rarely practical when community-level rates are required. Here we evaluate the chitobiase method as a rapid, routine and instantaneous method for measuring the productivity of freshwater crustacean zooplankton communities. Chitobiase, a moulting enzyme, is liberated into water following moulting and production rates are calculated by measuring its turnover rate in the water column. First, using literature-based instar- and stage-specific individual body mass values, we found a common relationship between post-moult body size (and individual chitobiase activity) and the biomass produced between successive moults for common freshwater groups. Secondly, using a time-series of weekly measurements in a North-Temperate lake, we found a good correspondence between the standing activity of chitobiase in the water column (CBANAT) and the biomass sampled by a plankton net and laser optical plankton counter (LOPC). Overall, however, CBANAT-based biomass more closely corresponded to LOPC-based biomass estimates. Lastly, depth-specific biomass production rates and daily production to biomass estimates varied positively with temperature. Daily production to biomass ratios also varied closely with predictions of a taxon-specific temperature-dependent model for freshwater zooplankton.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".