The need for sustained and enhanced international research efforts on zooplankton production
Bibliographic record
Abstract
Abstract The 7th International Zooplankton Production Symposium was held in Hobart, Australia in March, 2024 with the theme ‘New Horizons’, chosen to reflect the current perception that oceans are now experiencing greater human impacts than ever before, and to explore how zooplankton are impacted. Since zooplankton have a pivotal role as grazers of primary production and as prey for higher trophic levels, including both harvested fish species and marine mammals and seabirds, it is more important than ever to understand how zooplankton are responding to changes in marine ecosystems. Here, we provide an introduction to the special issue of papers resulting from the symposium, with details on the conference itself, the workshops and sessions convened and the main outcomes that the individual papers in this issue have contributed to. Several themes recurred through the week; new sampling techniques and the challenges of using both traditional and new methodologies, including digital data and the large datasets generated. There are still under-sampled and under-studied regions and taxa, creating knowledge gaps that complicate a full understanding, especially while there are numerous threats and stressors on zooplankton communities through pollution and climate change. Nevertheless, significant efforts continue to be made to advance the state of knowledge and the Zooplankton Production Symposia community is active, engaged and now looking forward to the eighth Symposium.
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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 | 0.053 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.012 | 0.014 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.006 | 0.009 |
| Insufficient payload (model declined to judge) | 0.023 | 0.005 |
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".