Resistances of Literature: Strategies of Narrative Affiliation in Etel Adnan's Sitt Marie Rose
Bibliographic record
Abstract
Water released from wood during transpiration (capacitance) can meaningfully affect daily water use and drought response. To provide context for better understanding of capacitance mechanisms, we investigated links between capacitance and wood anatomy. On twigs of 30 temperate angiosperm tree species, we measured day capacitance (between predawn and midday), water content, wood density, and anatomical traits, that is, vessel dimensions, tissue fractions, and vessel-tissue contact fractions (fraction of vessel circumference in contact with other tissues). Across all species, wood density (WD) and predawn lumen volumetric water content (VWC<sub>L-pd</sub> ) together were the strongest predictors of day capacitance (r<sup>2</sup><sub>adj</sub> = .44). Vessel-tissue contact fractions explained an additional ~10% of the variation in day capacitance. Regression models were not improved by including tissue lumen fractions. Among diffuse-porous species, VWC<sub>L-pd</sub> and vessel-ray contact fraction together were the best predictors of day capacitance, whereas among semi/ring-porous species, VWC<sub>L-pd</sub> , WD and vessel-fibre contact fraction were the best predictors. At predawn, wood was less than fully saturated for all species (lumen relative water content = 0.52 ± 0.17). Our findings imply that day capacitance depends on the amount of stored water, tissue connectivity and the bulk wood properties arising from WD (e.g., elasticity), rather than the fraction of any particular tissue.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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) | 0.000 | 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 teacher head, 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".