Bulk and Size-Fractionated Chlorophyll and Phaeopigment Concentrations Collected by Niskin Bottle, BC, Canada (Research)
Bibliographic record
Abstract
This dataset is comprised of Hakai Institute chlorophyll and phaeopigment concentrations collected from 2017 to present at stations within the Calvert Island, Johnstone Strait, and the Quadra Island study regions. Samples for these data were collected in the field using Niskin bottles. Sample water (250 ml) was filtered through: 1) a single glass fiber filter (GF/F, nominal pore size 0.7 um) to comprise a bulk measure of phytoplankton chlorophyll and phaeopigment concentrations and 2) a stack of filters (GF/F, 3 um, and 20 um polycarbonate filters) to estimate chlorophyll and phaeopigment concentrations from pico, nano, and micro-phytoplankton, respectively. Filters were extracted for 24 hours in 10 ml of 90% acetone and analyzed on Turner Designs Trilogy laboratory fluorometers using the acidification module and following the method of Holm Hansen et al. (1965). Data have been quality controlled by manual inspection which included a comparison of bulk concentrations against the sum of the size-fractionated filter concentrations. Where available, further comparisons were done with other measures of chlorophyll concentrations including in situ CTD chlorophyll fluorescence and high-performance liquid chromatography (HPLC) data. Data were collected by the Hakai Institute Oceanography and Nearshore programs.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.016 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.017 | 0.021 |
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".