Influence of the Growth Regulators Kinetin and 2,4-D on the Growth of Two Chlorophyte Microalgae, Haematococcus pluvialis and Dunaliella salina
Bibliographic record
Abstract
Haematococcus pluvialis Flotow and Dunaliella salina Teodoresco are commercially important because of their ability to accumulate very high carotenoid contents. However, their use is hindered by their slow growth rates. This paper reports a study on the effects of two growth regulators, 2,4-D (2,4-dichlorophenoxyacetic acid) and kin (kinetin), in concentrations of 0, 0.5, 1.0 and 2.0 mg l-1 each in a factorial design (24 combinations), as a possible means of enhancing the growth rates.After 12-13 days of treatment with plant hormones, D. salina showed a significant increase in growth with all the hormone concentrations and combinations used and under 15% salinity (NaCl, w/v), (except for 0.5 mg l-12,4-D and no kin), with up to 410% more cells than the control; under 10% salinity (NaCl, w/v), the increase in growth was significant with 0.5 mg l-1 2,4-D and no kin (180% more cells than the control), and also with 1.0 mg l-1 2,4-D and no kin (126% more cells than the control) and 2.0 mg l-1 2,4-D and 0.5 mg l-1 kin (134% more cells than the control) in the culture medium. Cultures of H. pluvialis were significantly influenced under 1.0 mg l-1 2,4-D (with 320% more cells than the control), but alsoshowed a significant increase in the growth rate when the ratio auxin to cytokinin was 1 (equal concentrations of 1.0 mg l-1 of both growth regulators) with more than 290% cells than the control, and with 0.5 mg l-1 2,4-D and 2.0 mg l-1 kin (200% more cells than the control) in the culture medium.
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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 | 0.000 | 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) | 0.001 | 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".