Sea wrack wet to dry biomass calibrations for macroalgae of the Central Coast of British Columbia - 2018
Bibliographic record
Abstract
This dataset contains species-specific standardized correction factors for wet-dry calibrations for several macroalgae species of the Central Coast of British Columbia. Macroalgae and macrophytes form the base of productive ecosystems in the Northeastern Pacific Ocean. Often, ecological research on macrophytes, macroalgae, and sea wrack requires the conversion of biomass from wet to dry to create consistency across investigations. This process, however, can be impractical, time consuming, and labour intensive. Samples of 12 common Northeastern Pacific Ocean seaweed species (Alaria marginata, Codium fragile, Egregia menziesii, Fucus distichus, Macrocystis pyrifera, Mazzaella spp., Nereocystis luetkeana, Pterygophora californica, Pyropia spp., Ulva spp., and the seagrasses Zostera marina and Phyllospadix spp.) were collected randomly and opportunistically in two states: wet and fresh, or aged and partially desiccated. Samples were weighed, dried in an oven, and weighed again in a laboratory as quickly as possible after collection. Dried samples were disposed of after dry measurements were taken. Detailed methods are available in the in the linked folder. We found that all species displayed a strong (R2 > 0.5) and predictable (p < 0.05) linear relationship between wet and dried conditions. Half of the aged samples did not have a significant relationship between partially desiccated and dried conditions. These results offer a reliable set of species-specific standardized correction factors for wet samples that can be used in future macrophyte, macroalgae, and sea wrack research, reducing the need to conduct extensive wet-dry calibrations in future studies. Contributors: Sara Wickham – University of Victoria, Brian Starzomski – University of Victoria, John Reynolds – Simon Fraser University, Chris Darimont – University of Victoria
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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 | 0.003 | 0.006 |
| 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) | 0.010 | 0.006 |
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".