FDmine database: A trait-based approach in the study of functional diversity of deep-sea environments targeted for mineral exploitation
Bibliographic record
Abstract
The increasing exploitation of marine resources has prompted concerns about the health of our oceans, including deep-seabed ecosystems which not only hold high and unique biodiversity but also substantial deposits of mineral resources. Commercial mining has not yet begun, but national and international governing agencies are under pressure to rapidly develop: 1) science-based regulatory frameworks for environmental impact assessments, and 2) environmental management and monitoring plans that safeguard the protection of the biodiversity and services provided by these unique ecosystems. Environmental impact assessments have traditionally used taxonomic-based indicators of ecosystem integrity. However, growing evidence advocates for a parallel assessment of disturbance effects on the functions provided by biological communities. Functional diversity metrics rely on measurable biological traits of an organism and inform on that organism’s influence and response to environmental changes and/or its effects on ecosystem processes. Thus, functional trait analyses have the potential to offer new insights into how to develop and prioritize management and conservation strategies for various ecosystems at local, regional, and global scales. This dataset establishes a common framework for functional trait analyses in deep-sea ecosystems targeted for seabed mining.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.020 | 0.020 |
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".