Understory kelp biomass data from BC Central Coast
Bibliographic record
Abstract
The understory kelp biomass dataset is a component of Hakai Institute’s Nearshore research and monitoring program. This dataset characterizes spatial and temporal trends in understory kelps and kelp-consuming invertebrate communities at 22 rocky bottom subtidal sites near Calvert Island on the Central Coast of British Columbia, Canada (2014-now). Data coverage varies by year. The total length and abundance of all kelps, Desmarestia species, and urchins were measured at 6 sites seasonally (5 times within a year) from 2014-2018. Urchin behaviour was also recorded. From 2016-current, additional sites (4-15) were surveyed annually using a variation on the sampling design (reduced measurements on a subset of sites). In 2018-current kelp consumer species abundance, including small mobile grazing invertebrates, were recorded. Dry weight values and length to weight relations are also provided to extrapolate density and morphology data to biomass. Spatial and temporal variability in kelp and invertebrate community relationships is described. This data package is freely available to everyone, following the principles of equitable access and benefit sharing. However, we expect all data users to give attribution to the data providers (read our data license) and the use of these data should happen in the light of fair use, i.e.: 1) respect the data providers, and provide helpful feedback on data quality, and 2) communicate and/or collaborate with the providers if you are considering using this dataset for manuscripts or other forms of reporting.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.008 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.019 | 0.017 |
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".