Programovatelný LED panel pro velko-objemovou kultivační třepačku INNOVA (New Brunswick)
Bibliographic record
Abstract
Cultivation is one of the basic methods required for physiological experiments studying the growth and division of microorganisms, including algae and cyanobacteria, and the effect of changes in growth conditions. Furthermore, cultivation of cyanobacteria and algae is an essential prerequisite for biochemical and molecular-biological analyses in most of photosynthetic studies. Such experiments often require well controlled cultivations of large volumes of cell cultures (liters). INNOVA laboratory shakers (New Brunswick) have very good reputation and are used almost as a standard (but not a cheap) equipment in biochemical and mol-biol laboratories. The cultivation of photosynthetic organisms under photoautotrophic conditions is possible in INNOVA shaker, however the standard bulb panel installed inside the shaker, is problematic. The size of the panel is much less than the size of shaker platform, which causes gradient of light intensity from the middle of the platform (160 microE photons) to each side (30 microE photons). Also, this bulb panel is not provided with a regulation of light intensity and together these parameters very limit the usability of the shaker for the cultivation of photosynthetic organisms.\n\nWe constructed LED panel, which size is exactly of the size of shaker lid and the panel is covered evenly by LED strips. Light intensity is thus very evenly distributed over the entire surface of the platform and can be set to any value from 0 - 400 microE photons. Light – dark cycles (panel on/off) can be also programmed. Combination of this light panel and INNOVA shaker (a precise setting of rpm and temperature) is a powerful tool for the preparation of large volumes of photosynthetically grown liquid cultures under strictly controlled growth conditions. \n\n
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.053 | 0.017 |
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".