Going with the Flow
Bibliographic record
Abstract
Trees, scientists, artists, and their instruments sense how water flows. Trees take risks during the growing season by boosting transpiration (sap flow) or they hold back. Veins expanding, contracting. Water is pulled upwards from the soil to the air, passing by tree stems. But if drought occurs, trees could die. Scientists and trees are both sensing in tandem with environmental conditions. Because of its regular fluctuations, the process can be compared to the pulse of a heart. During daytime, trees transpire, resulting in stem water movement. This decreases from the outer to the inner sapwood, and because of dehydration, stem shrinkage ensues. During the night, the stem swells due to rehydration and water recharge. Pulling, funneling, decreasing, expanding. The pulsing relays these functions in relation with light and atmospheric humidity. Scientists monitor the tree stem with heated needles, and encircle the tree trunks with straps and machines. A dendrometer quantifies the changes in the stem's size due to swelling, shrinkage, and growth, rendering it in microns. On July 3, 2020, for a mature sugar maple tree, its diameter was 355000.13 μm at 3:00 pm and 355062.47 μm at midnight. On the same day and for the same tree, a sap flow sensor combined with a data logger and algorithms reported the transpiration in centimeters per hour : 1.05 cm/h at 5:00 am and 14.89 cm/h at 2:00 pm. The sampling concurs, the tree is constantly changing. The tree's experience is shared with humans. Building together, from experience, the knowledge about and in changing climates, opening up to multiplicities, embracing ecotechnologies in practice: the tree's own, the instrumentation and the milieu of co-occurrence. Visualizations weave relations of this meeting between quantitative and qualitative sensing, an interoperability with computers. Is this data fidelity? Here, sap flow becomes a series of dots, densifying, the tree water deficit is inferred by the changing dimensions of the intervals between lines, pointing to local changes and changes in the tree. Still. Flowing. On.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.008 | 0.009 |
| Scholarly communication | 0.013 | 0.020 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.090 | 0.025 |
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".