Harmonizing marine zooplankton trait data toward a mechanistic understanding of ecosystem functioning
Bibliographic record
Abstract
Abstract Compiling trait information promotes discovery and innovation in using trait‐based approaches in ecology. Various zooplankton trait datasets are stored in unlinked data repositories, in diverse data structures, and have varying levels of complexity. These require standardization and harmonization to allow interoperability and to limit the duplication of efforts in the time‐consuming and error‐prone task of trait compilation. This study aggregated and harmonized 33 zooplankton traits datasets and supplemented these with more than 150 references into a single zooplankton trait database with an initial set of 56 traits for 3535 marine zooplankton species. The database has a long data table structure using the entity‐attribute‐value format and includes taxonomic and ancillary metadata, and data source provenance preserving how the data were originally recorded. The database is stored both at the individual level (Level 1) and as species level means (Level 2). The Level 1 database has 57,615 rows of trait records and the Level 2 database has 14,977 unique trait‐taxon records. We evaluated the coverage of trait data, taxonomic representation, and strategies in filling‐in data gaps. Comparison of trait value estimation approaches identified allometric scaling to be more accurate than taxon‐level generalization and imputation. This centralized and harmonized marine zooplankton trait database aims to be extendable and future‐proof and to promote trait data sharing, FAIR (findable, accessible, interoperable, reusable) data practices, and reproducibility.
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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 | 0.009 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 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".