Harmonizing marine zooplankton trait data toward a mechanistic understanding of ecosystem functioning
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it