PFTC6 - PFTCourses plant functional traits, carbon fluxes, temperatures, and spectral data from climate and global change experiments along climate gradients in Western Norway
Bibliographic record
Abstract
Plant functional trait-based approaches are powerful tools to assess the consequences of global environmental changes for plant ecophysiology, population and community ecology, ecosystem functioning, and landscape ecology. Here, we present data capturing these ecological dimensions from grazing, nitrogen addition, and warming experiments conducted along a 821 m a.s.l. elevation gradient and from a climate warming experiment conducted across a 3,200 mm precipitation gradient in boreal and alpine grasslands in Vestland County, Norway. From these systems we collected 28,762 plant and leaf functional trait measurements from 76 vascular plant species, 88 leaf assimilation-temperature responses, 577 leaf handheld hyperspectral readings, 2.26 billion leaf temperature measurements, 3,696 ecosystem CO2 flux measurements, and 10.69 ha of multispectral (10-band) and RGB cm-resolution imagery from 4,648 individual images obtained from airborne sensors. These data augment existing longer-term data on local climate, soils, plant populations, plant community composition, and ecosystem functioning from within the same experiments and study systems and from similar systems in other mountain regions globally.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.023 | 0.023 |
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".