The dimensional data of American beech trees in changing climates: environmental data science meets digital art installations in public space
Bibliographic record
Abstract
Climate change is undoubtedly one of the greatest challenges for humanity to address. The IPCC synthesis report (2023) provides guidance; yet the constant barrage of adverse impacts seems to paralyze citizens who recoil from taking action under the pressure of constant and rapidly approaching “tipping points”, thresholds between two states. Explored through the lens of “dimensional data”, understood as both a measurement and a quality, the data shows the resilience of another living being, the beech tree, which can sustain repeated short but intense periods of drought. The climate science data analysis is culled in the academic research site of a forest near Tiohtià:ke/Mooniyang/Montreal (Quebec, Canada). Grounded in and extending a numerical approach, beech tree thresholds are shared with publics through an art-science collaboration and digital art creativity. From soil water potential and soil temperature sensors compiled from 2017 to 2020, the data reveal the tree’s own dimensional relations to its environment which is subsequently shared artistically in the dynamic visualizations of a large-scale outdoor art installation entitled Beech-Becomings presented in a rural forest in May 2023.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".