A new minimum fluorescence parameter, as generated using pulse frequency modulation, compared with pulse amplitude modulation: F α versus F o
Bibliographic record
Abstract
The minimum fluorescence parameter (Falpha), generated using the new pulse frequency modulation (PFM) technology, was compared with the minimum fluorescence parameter (Fo), generated by pulse amplitude modulation (PAM), in response to a reversible low-oxygen stress in 'Honeycrisp'trade mark (HC) apples (Malus domestica) and an irreversible osmotic stress induced by water loss in two grape (Vitis spp.) cultivars ('L'Acadie' (LAc) and 'Thompson Seedless' (TS)). The minimum fluorescence values produced by both fluorometer types in response to a reversible low-oxygen stress in apples were indistinguishable: both Fo and Falpha increased when O2 levels were lowered below the anaerobic compensation point (ACP); when gas levels returned to normoxia both parameters dipped below, then returned to, the original fluorescence baseline. The two parameters also responded similarly to the irreversible osmotic stress in grapes: in both cultivars, Falpha and Fo first decreased before reaching an inflection point at approximately 20% mass loss and then increased towards a second inflection point. However, the two parameters were not analogous under the irreversible osmotic stress; most notably, the relative Falpha values appeared to be lower than Fo during the later stages of dehydration. This was likely due to the influence of the Fm parameter and an overestimation of Falpha when measuring the fluorescence from healthy and responsive chloroplasts as found in grapes experiencing minimal water loss, but not in grapes undergoing moderate to severe dehydration. An examination of the data during a typical PFM scan reveals this fluorometer system may yield new fluorescence information with interesting biological applications.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".