Mesopelagic Mesozooplankton and Micronekton Database
Bibliographic record
Abstract
The Mesopelagic Mesozooplankton and Micronekton Database (MMMD) compiles quantitative data on the distribution and density of mesopelagic (200-1000 m) mesozooplankton and micronekton (0.2-20 mm and 20-200 mm) species, using 258 published and unpublished sources spanning from 1880 to 2016. This extensive dataset includes 266,611entries, covering a broad temporal, spatial range and diel changes with varying levels of completeness. The data were standardized to address inconsistencies in sampling methods, mesh sizes, and taxonomic classifications. Despite some limitations, including gaps in spatial and depth coverage, the database provides a valuable resource for the mesopelagic macro-ecological research, which is updatable. Comparisons with existing databases underscore its unique contributions to the study of mesopelagic ecosystems.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.004 |
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".