Evergreen needleleaf forest pigment, MONI-PAM, eddy-covariance, and tower-scale remote sensing data across four different sites
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.006 |
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 it