Novel high‐throughput oxygen saturation measurements for quantifying the physiological performance of macroalgal early life stages
Bibliographic record
Abstract
Abstract Understanding how macroalgal forests will respond to environmental change is critical for predicting future impacts on coastal ecosystems. Although measures of adult macroalgae physiological responses to environmental stress are advancing, measures of early life‐stage physiology are rare, in part due to the methodological difficulties associated with their small size. Here we tested a novel, high‐throughput method (rate of oxygen consumption and production; ) via a sensor dish reader microplate system to rapidly measure physiological rates of the early life stages of three habitat‐forming macroalgae, the kelp Ecklonia radiata and the fucoids Hormosira banksii and Phyllospora comosa. We measured the rate of O2 consumption (respiration) and O2 production (net primary production) to then calculate gross primary production (GPP) under temperatures representing their natural thermal range. The microplate system was suitable for rapidly measuring physiological rates over a temperature gradient to establish thermal performance curves for all species. The microplate system proved efficient for measures of early life stages of macroalgae ranging in size from approximately 50 μm up to 150 mm. This method has the potential for measuring responses of early life stages across a range of environmental factors, species, populations, and developmental stages, vastly increasing the speed, precision, and efficacy of macroalgal physiological measures under future ocean change scenarios.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".