Particulate organic matter composition for freshwater and marine stations from 2015 through 2018 on the Central Coast, British Columbia, Canada.
Bibliographic record
Abstract
This data package includes two datasets used to assess spatial and temporal patterns in particulate organic matter (POM) chemical composition in surface (0-10 m) waters at 11 oceanographic stations along British Columbia’s Central Coast, associated with St. Pierre et al. (2022, submitted). The datasets within the data package include: ‘Oceanographic station mixture dataset.csv’: Particulate organic carbon (POC) and particulate nitrogen (PN) concentrations and stable isotope signatures (δ13C, δ15N) and ratios of carbon-to-nitrogen (C:N) in surface waters (0-10 m) across 11 oceanographic stations, sampled during routine monthly surveys conducted between January 21st 2015 and September 11th 2018. Also includes water temperature, salinity, chlorophyll a concentrations, and POC-to-chlorophyll a ratios. ‘End-member raw data.csv’: Particulate organic carbon (POC) and particulate nitrogen (PN) concentrations and stable isotope signatures (δ13C, δ15N) and ratios of carbon-to-nitrogen (C:N) in marine phytoplankton, terrestrial, and marine intertidal macrophyte POM sources. Note that means ± standard deviations for each source were used in the Bayesian mixing model presented in St. Pierre et al. (in review). Marine intertidal macrophyte data are a subset from Froese et al. (2019). Sample collection and processing information can be found in: St. Pierre, K.A., Hunt, B.P.V., Giesbrecht, I., Tank, S.E., Lertzman, K.P., Del Bel Belluz, J., Hessing-Lewis, M.L., Olson, A., Froese, T. Marine particulate organic matter chemistry reflects seasonally and spatially variable contributions from terrestrial, intertidal and marine pelagic sources in the Northeast Pacific coastal temperate rainforest. (In review) Information on the collection of the marine intertidal macrophyte data can be found in: Froese, T., Sadlier-Brown, G., Olson, A.M., and Hessing-Lewis, M. (2019). Nearshore macrophyte stable isotope results from BC’s Central Coast, 2014-2018. Hakai Institute dataset. https://doi.org/10.21966/q31x-qg72
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.010 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.018 | 0.007 |
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".