Diagnosis and correction of soil nutrient limitations in intensively managed Southern pine forests. Quarterly report for the period January-March 2000
Bibliographic record
Abstract
This report is a summary of project status and activities performed during the quarter: (1) Initial error runs were finished and coding was adjusted to account for errors. A documentation manual and users manual have been written and gone through the first editing. The model was presented to the forest industry during a review meeting and adjustments were made to the model based on their input. The model has been sent to interested parties in Canada and Australia to test it and report back on any errors or modifications they feel would be necessary. (2) Based on the laboratory studies, one paper was written and submitted to the Soil and Water Science for internal review. It is currently under internal review. This paper describes a method for measuring resorption isotherms. A second paper that uses this method to investigate desorption isotherms for different soils is in progress. (3) All soil samples have been dried and sieved. All tissue samples have been ground and the ashing of the tissue samples has just begun. (4) A literature search continued focusing on root to shoot relationships of fast growing pine in order to assist the development of the nutrient demand section of the model. This is about 40% done.
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.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".