Evergreen needleleaf forest pigment, MONI-PAM, eddy-covariance, and tower-scale remote sensing data across four different sites
Bibliographic record
Abstract
The data presented here are from four evergreen needleleaf forests, which include boreal forest locations in Alaska (DEJU, mean annual temperature = 0.4 degrees Celsius [°C], latitude = 63.9 degrees north [°N]) and Saskatchewan, Canada (Ca-Obs, 1.3°C, 54.0°N), a high elevation forest in Colorado (US-NR1, 2.8°C, 40.0°N), and a longleaf pine forest in Florida (OSBS, 21.1°C, 29.7°N). Included are needle-scale pigment data from the DEJU, US-NR1, and OSBS sites; MONI-PAM fluoresence data from the DEJU and US-NR1 sites, tower-scale eddy-covariance, meterological, and remotely sensed solar-induced fluoresence and vegetation index data across all four sites. More information on these data can be found in the accompanying publications: Pierrat, Z.A., Magney, T., Maguire, A., Brissette, L., Doughty, R., Bowling, D.R., Logan, B., Parazoo, N., Frankenberg, C., Stutz, J., 2024. Seasonal timing of fluorescence and photosynthetic yields at needle and canopy scales in evergreen needleleaf forests. Ecology 105, e4402. https://doi.org/10.1002/ecy.4402 Pierrat, Z.A., Magney, T.S., Cheng, R., Maguire, A.J., Wong, C.Y.S., Nehemy, M.F., Rao, M., Nelson, S.E., Williams, A.F., Grosvenor, J.A.H., Smith, K.R., Reblin, J.S., Stutz, J., Richardson, A.D., Logan, B.A., Bowling, D.R., 2024. The biological basis for using optical signals to track evergreen needleleaf photosynthesis. BioScience 74, 130–145. https://doi.org/10.1093/biosci/biad116 This version of the dataset includes longer timeseries of MONI-PAM fluoresence data used in Pierrat et al., 2024 Ecology. Users of the data are highly encouraged to contact the data producers for futher information on usage and limitations of this dataset.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| 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) | 0.001 | 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".