FPA 2017. State of the forests Tasmania 2017
Bibliographic record
Abstract
The Forest Practices Authority is required to report every five years on the state of Tasmania’s forests pursuant to section 4Z of the Forest Practices Act 1985. Previous such reports have been prepared and released in 2002, 2007 and 2012. The reporting period for this report is nominally 1 July 2011 to 30 June 2016. The Tasmanian Regional Forest Agreement (RFA) between the State of Tasmania and the Commonwealth of Australia was signed on 8 November 1997. Clause 91 of the RFA required the Parties to develop agreed sustainability indicators. A key requirement was that the indicators should have regard to the Montreal Process Criteria and Indicators2 as amended from time to time. A set of indicators was developed and released in June 2000. It was also agreed that the Governments would prepare a joint report against the indicators on a five yearly basis timed to inform each five year review of the RFA as required by clause 45 of the RFA. The State of the forests Tasmania 2017 has as its framework 42 Sustainability Indicators, and therefore also serves to meet the requirements for reporting under the Montreal Process for the five yearly reviews of the Tasmanian RFA. Major changes have occurred in Tasmania’s forests and forest industry since the previous reporting period (2006-11), and these have impacted on the structure of the data presented in this report. A summary of the legislative, tenure and forest management changes which have occurred is given in the Introduction.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.090 | 0.041 |
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".