Controlled Environments In The New Millennium,
Bibliographic record
Abstract
The option to measure and control carbon dioxide levels within controlled environment chambers has been commercially available for over a decade. Despite this fact, relatively few controlled environment users choose to purchase or even utilise this option when it is available on their equipment. Routine measurements taken at the McGill University Phytotron have shown pronounced fluctuations and significant variability of CO2 levels between replicate growth chambers, between plant developmental stages and over the course of diurnal and seasonal cycles. Since elevated and below-ambient CO2 levels have well documented effects on physiological, morphological and developmental aspects of plant growth, poor control of this variable must influence experimental results and the validity of research findings. At the McGill University Phytotron, chamber-mounted Infra-Red Gas Analysers are utilised in combination with simple scrubbers and injected CO2 gas to control and stabilise ambient CO2 levels. Typical long-term results demonstrate consistent CO2 control at a level of ± 10 µmol·mol-1. A description of the materials, maintenance requirements and costs associated with routine control of this variable will be presented.
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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.030 | 0.006 |
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".