The bias of a two‐dimensional view: comparing two‐dimensional and three‐dimensional mesophyll surface area estimates using noninvasive imaging
Bibliographic record
Abstract
Summary The mesophyll surface area exposed to intercellular air space per leaf area (Sm) is closely associated with CO2 diffusion and photosynthetic rates. Sm is typically estimated from two‐dimensional (2D) leaf sections and corrected for the three‐dimensional (3D) geometry of mesophyll cells, leading to potential differences between the estimated and actual cell surface area. Here, we examined how 2D methods used for estimating Sm compare with 3D values obtained from high‐resolution X‐ray microcomputed tomography (microCT) for 23 plant species, with broad phylogenetic and anatomical coverage. Relative to 3D, uncorrected 2D Sm estimates were, on average, 15–30% lower. Two of the four 2D Sm methods typically fell within 10% of 3D values. For most species, only a few 2D slices were needed to accurately estimate Sm within 10% of the whole leaf sample median. However, leaves with reticulate vein networks required more sections because of a more heterogeneous vein coverage across slices. These results provide the first comparison of the accuracy of 2D methods in estimating the complex 3D geometry of internal leaf surfaces. Because microCT is not readily available, we provide guidance for using standard light microscopy techniques, as well as recommending standardization of reporting Sm values.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".