Estimating forest productivity in the eastern Siskiyou Mountains of southwestern Oregon using a satellite driven process model, 3-PGS
Bibliographic record
Abstract
The 3-PGS (physiological principles for predicting growth using satellite data) model generates monthly estimates of transpiration, photosynthesis, and net primary production (NPP), the latter derived as a fixed proportion (0.47) of gross photosynthesis. To assess the reliability of a simplified process model (3-PGS) to predict the productive capacity of coniferous forest across diverse landscapes in southwestern Oregon, we first used a geographic information system to display and manipulate basic data. This involved the following steps: (i) extrapolate monthly mean weather data to reflect topographic variation; (ii) transform monthly temperature extremes to spatial resolution of 4 ha and estimate incoming solar radiation, subfreezing days per month, daytime vapor pressure deficits, and mean temperatures; (iii) convert statewide soil survey maps into topographically adjusted estimates of soil fertility and water storage capacity (θ); and (iv) acquire satellite-derived estimates of the faction of light intercepted by vegetation during midsummer. Model predictions of soil water availability during summer months compared well with those reported from published measurements of predawn water potentials at three contrasting sites and with measurements acquired at the end of seasonal drought at 18 sites (r2= 0.78 with mean monthly modeled drought index; r2= 0.57 with seasonal modeled drought index). Similarly, seasonal shifts in the relative importance of various climatic and edaphic variables closely matched those defined in previously published studies. Finally, model predictions of maximum annual aboveground growth were compared with those derived from forestry yield tables based on height-age relationships with a resulting r2of 0.76, and a standard error of 1.2 m3·ha-1·year-1(P < 0.01).
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".