Structural and functional effects of global invasion pressure on benthic marine communities – patterns, challenges and priorities
Bibliographic record
Abstract
Here we present datasets underlying the results of the study examining patterns of structural and functional community-level change in a range of well-studied marine ecosystems with documented histories of bioinvasion. For the purpose of the study, the authors identified six regions with extensive bay-scale datasets on native and non-indigenous benthic species assemblages, allowing paired comparisons in time (years to decades; retrospective datasets) or space (high vs. low proximity to hotspots of NIS introductions within a region; cross-sectional datasets) representing differences in bioinvasion pressure (Table 1). These sites were: 1) coastal waters of British Columbia, Canada (BC); 2) San Francisco Bay, USA (SF); 3) Ilha Grande Bay, Brazil (BR); 4) North-Eastern Baltic Sea, Estonia (BS); 5) estuaries of New South Wales, Australia (AU) and 6) Waitematā Harbour, New Zealand (NZ). These six regions are generally at mid- to higher-latitudes, with one low-latitude site (BR). The study sites encompassed surveys of benthic communities: fouling assemblages (BC and AU), subtidal reefs (BR and BS), and soft-sediment benthos (SF, BS, and NZ). Three data files are provided for each study region and include: benthic community data (XX_species.csv) binary (0/1) biological trait information compiled for all species in the dataset (XX_traits.csv) additional explanatory variables considered for each study region - bioinvasion pressure and spatial variables (XX_env.csv) For detailed information on datasets and methods description please refer to the original manuscript by Zaiko et al.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".