Diatom growth responses to photoperiod and light are predictable from diel reductant generation
Bibliographic record
Abstract
Light drives phytoplankton productivity, so phytoplankton must exploit variable intensities and durations of light exposure, depending upon season, latitude, and depth. We analyzed the growth, photophysiology and composition of small, Thalassiosira pseudonana, and large, Thalassiosira punctigera, centric diatoms from temperate, coastal marine habitats, responding to a matrix of photoperiods and growth light intensities. T. pseudonana showed fastest growth rates under long photoperiods and low to moderate light intensities, while the larger T. punctigera showed fastest growth rates under short photoperiods and higher light intensities. Photosystem II function and content responded primarily to instantaneous growth light intensities during the photoperiod, while diel carbon fixation and RUBISCO content responded more to photoperiod duration than to instantaneous light intensity. Changing photoperiods caused species‐specific changes in the responses of photochemical yield (e−/photon) to growth light intensity. These photophysiological variables showed complex responses to photoperiod and to growth light intensity. Growth rate also showed complex responses to photoperiod and growth light intensity. But these complex responses resolved into a close relation between growth rate and the cumulative daily generation of reductant, across the matrix of photoperiods and light intensities.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".