Parameterizing and operationalizing zooplankton population dynamic and trophic interaction models
Bibliographic record
Abstract
Abstract Plourde, S., and Browman, H. I. 2014. Parameterizing and operationalizing zooplankton population dynamic and trophic interaction models. – ICES Journal of Marine Science, 71: 234–235. This themed set (TS) of articles was motivated by the need for modellers and biologists–ecologists to work more closely together to produce more realistic simulation models of zooplankton population dynamics and trophic interactions. The TS was intended to cover a broad range of subjects and potential approaches, including identifying crucial gaps in our knowledge and parameterization of biological/physiological processes, experimental/fieldwork aimed at quantifying the response of key physiological and behavioural processes to variations in the environment, identifying novel modelling approaches that would enable the development of simulation models that would minimize the need for species-specific (and stage-specific) model parameterization, etc. Five articles were accepted for inclusion in the TS. They cover the majority of these themes. TSs are intended to be instrumental in focusing attention, triggering opinions, and stimulating ideas, discussion and activity in specific research fields. We hope that this TS has achieved that.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.015 | 0.062 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".