Gas exchange analysers exhibit large measurement error driven by internal thermal gradients
Bibliographic record
Abstract
Summary Portable gas exchange analysers provide critical data for understanding plant‐atmosphere carbon and water fluxes, and for parameterising Earth system models that forecast climate change effects and feedbacks. We characterised temperature measurement errors in the Li‐Cor LI‐6400XT and LI‐6800, and estimated downstream errors in derived quantities, including stomatal conductance (gsw) and leaf intercellular CO2 concentration (Ci). The LI‐6400XT exhibited air temperature errors (differences between reported air temperature and air temperature measured near the leaf) up to 7.2°C, leaf temperature errors up to 5.3°C, and relative errors in gsw and Ci that increased as temperatures departed from ambient. This caused errors in leaf‐to‐air temperature relationships, assimilation–temperature curves and CO2 response curves. Temperature dependencies of maximum Rubisco carboxylation rate (Vcmax) and maximum RuBP regeneration rate (Jmax) showed errors of 12% and 35%, respectively. These errors are likely to be idiosyncratic and may differ among machines and environmental conditions. The LI‐6800 exhibited much smaller errors. Earth system model predictions may be erroneous, as much of their parametrisation data were measured on the LI‐6400XT system, depending on the methods used. We make recommendations for minimising errors and correcting data in the LI‐6400XT. We also recommend transitioning to the LI‐6800 for future data collection.
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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".