A tool for converting forest ecosystem classifications for permanent or temporary growth plots into the new provincial Ecological Land Classification (ELC) system in the boreal regions of Ontario
Bibliographic record
Abstract
Forest Ecosystem Classification (FEC) systems were originally developed and used in the Province of Ontario at regional levels with the objective of classifying forest ecosystems to support silvicultural decision-making in an operational setting. A new provincial Ecological Land Classification (ELC) system has been developed, which integrates the regional systems into a single consistent framework. To make continued use of data and knowledge gained from long-term forest monitoring plots classified under the old FEC system, such as Permanent Growth Plots (PGPs), Permanent Sample Plots (PSPs) and Temporary Sample Plots (TSPs), an ecosite conversion from the existing FEC data to the new ELC system was deemed necessary. We developed a conversion matrix to convert FEC ecosite classifications from the northeast and north-west to the provincial ELC system for the boreal forest region of Ontario. The conversion system is intended to apply at the scale of individual plots, with a special focus on PGP, PSP and TSP networks, and has been limited to forested ecosystems, as FEC systems were originally developed for the forest land base only. The conversion is primarily driven by canopy cover composition derived from plot-based individual tree data, with additional information required on substrate characteristics (e.g., substrate type, depth of mineral material, effective soil texture and moisture regime). It is possible to derive some of the soil variables from the broadly defined soil-type (S-type) categories of the original FEC systems; however, this approach requires making some assumptions that could reduce the accuracy of conversion. We anticipate that this conversion matrix will bridge the gap until active plot networks are re-typed in the field into the ELC system, provide a link to historical TSPs, and would be of general interest to a variety of new ELC users.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".