Replication Data for: Effect of temporal aggregation and phenology on LUE model variables and productivity in two deciduous forests.
Bibliographic record
Abstract
The integration of optical and flux data requires temporal aggregation of data. In this study, we explore the effect of temporal integration through an in-depth assessment exploring its impact and its ecological relevance. Optical remote sensing and eddy covariance flux data collected over four years from a Tropical Dry Forest (TDF) and a Deciduous Boreal Forest (DBF) were used in the analysis. Light use efficiency model variables (PAR, fAPARgreen, APARgreen, and LUE) were derived, temporally aggregated over the diurnal period at 1hr increasing intervals and compared to gross primary productivity, derived from flux measurements. Temporal aggregation analysis was performed at the seasonal scale as well as divided by phenological stage (greenup, maturity, and senescence). Seasonal aggregation analysis showed little effect on fAPARgreen derived from TDF and DBF. Aggregation of PAR, APARgreen and LUEgreen variables from TDF and DBF showed significant changes in correlation to GPP. Aggregation analysis by phenology produced contrasting results to seasonal aggregations. This was especially the case for TDF fAPAR during greenup, where aggregation produced significant changes in correlation to GPP (Δr2 ≈ 0.34-0.39, p < 0.05). A relative importance analysis provided and systematic analysis of seasonal and phenological contributions of LUE model variables to ecosystem productivity. Previous knowledge of seasonal and phenological ecosystem functions was combined with relative importance metrics to put ecological context on our results. Findings confirm that physiological and structural contributions of the LUE model change between vegetation, environmental condition and phenology.
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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 | 0.006 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.003 |
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".