An alternative kernel-based method for estimating copepod growth rates from multimodal biomass distributions in artificial cohort experiments
Bibliographic record
Abstract
Abstract Background Elucidating copepod growth and production rates is important in understanding the trophic role of copepods in marine systems. One of the most commonly used field methods for measuring copepod growth rates is the artificial cohort method. Results An ‘artificial cohort’ is established by limiting the incubated animals to relatively narrow size ranges or developmental stages. Thus, one can measure the change in body weight (BW; attributed to body growth) between the start and conclusion of incubation by following the shift in the biomass distribution. The critical issue here is determining how to describe the biomass distribution before and after incubation in a mathematically concise manner. Traditionally, the mean value of the biomass distribution is used as the representative BW, which assumes that the biomass distribution follows a unimodal distribution. However, in practice, the complex composition of copepod communities can commonly yield observations of multimodal distributions. To overcome this difficulty, we suggest that the representative BW of copepod communities be estimated by multiple-peak consideration instead of using the average value. Specifically, we used a kernel-based approach to determine peak values; as such, only BW values associated with a high frequency were used to determine the representative BW. Conclusions Through a comparison of different methods, we show that the multiple-peak consideration yielded a higher proportion of realistic growth rate values. In addition, we noted that growth rates estimated with the multiple-peak method were more closely aligned with predictions based on the metabolic theory of ecology.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".